Device for modifying various types of assets

ABSTRACT

An asset modification device includes memory, an asset modification module, and operation modules. The memory stores limit tables and asset operational data. The asset modification module selects an asset to modify, a limit table regarding the asset, an operational module based on an entry in the limit table, and evaluation data. A specific task execution module of the selected operation module executes a specific task on asset operational data of the asset to produce a modified asset when an evaluation data filter of the selected operation module indicates that analysis of the evaluation data is favorable for modification of the asset via the specific task.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 USC §119(e) to a provisionally filed patent application entitledASSET MODIFICATION COMMUNICATION SYSTEM AND COMPONENTS THEREOF, having aprovisional filing date of May 2, 2012, and a provisional Ser. No.61/641,723 (Attorney Docket No. IMW001), which is incorporated byreference herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

This invention relates generally to communication systems and moreparticularly to asset evaluation and modification within thecommunication system.

2. Description of Related Art

Communication systems are known to allow data to be from one or moredevices within a communication system to one or more other devices. Thedata may be raw data (e.g., created by a first communication device andcommunicated through the communication system unaltered), encrypteddata, compressed data, processed data (e.g., data created by a firstcommunication device is processed (e.g., manipulated, calculated,operand of a calculation, encoded, encrypted, compiled, etc.) by anotherdevice within the communication system as the data is communicatedthrough the communication system), etc. The data may be video data,audio data, text data, graphics data, image data, and/or a combinationthereof.

Almost every business, if not every business, uses data and communicatesdata with its customers, suppliers, employees, contractors, etc. Thedata may be advertisements to customers, purchase orders to suppliers,invoices to customers, accounting information, business evaluationinformation, inventory monitoring information, day trading information,market analysis information, etc. Depending on the type of business, abusiness may utilize large and expensive computer enterprise systems tomanage its data. For a small business or for an individual, it may useone or more personal computers and one or more software applications tomanage its data. Whether an individual, a small business, or a largebusiness, managing data is an ever increasing challenge as the amountand communication of digital data is increasing rapidly.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a datacommunication system in accordance with the present invention;

FIG. 1A is a schematic block diagram of an embodiment of a user and/orservice provider device in accordance with the present invention;

FIG. 1B is a schematic block diagram of an embodiment a computerreadable storage medium in accordance with the present invention.

FIG. 2 is a schematic block diagram of an embodiment of an assetmodification module in accordance with the present invention;

FIG. 3 is a diagram of an example of modifying an asset based onevaluation data in accordance with the present invention;

FIG. 3A is a diagram of an example of modifying an asset based onevaluation data in accordance with the present invention;

FIG. 4 is a schematic block diagram of an example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 5 is a logic diagram of a method for modifying an asset inaccordance with the present invention;

FIG. 5A is a logic diagram of a method for modifying an asset inaccordance with the present invention;

FIG. 5B is a logic diagram of a method for modifying an asset inaccordance with the present invention;

FIG. 6 is a schematic block diagram of an example of asset modificationoperations in accordance with the present invention;

FIG. 7 is a schematic block diagram of an embodiment of assetmodification operation in accordance with the present invention;

FIG. 8 is a schematic block diagram of another embodiment of assetmodification operation in accordance with the present invention;

FIG. 8A is a schematic block diagram of an embodiment of evaluation datafilter in accordance with the present invention;

FIG. 9 is a diagram of an example of an operation set limit table inaccordance with the present invention;

FIGS. 10A-10C are diagrams of an example of asset modification inaccordance with the present invention;

FIG. 11 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 12 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 13 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 14 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 15 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 16 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 17 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 17A is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIGS. 18-20 are a logic diagram of another method for modifying an assetin accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of an assetmodification module selecting a limit table for asset modification inaccordance with the present invention;

FIG. 22 is a diagram of an example of asset factors in accordance withthe present invention;

FIG. 23 is a diagram of an example of attributes in accordance with thepresent invention;

FIG. 24 is a logic diagram of a method of an asset management functionin accordance with the present invention;

FIG. 25 is a logic diagram of a method of another asset managementfunction in accordance with the present invention;

FIG. 26 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 29 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 30 is a schematic block diagram of another embodiment of an assetmodification module in accordance with the present invention;

FIGS. 31-33 are a logic diagram of another method for modifying an assetin accordance with the present invention;

FIG. 34 is a diagram of another example of an operation set limit tablein accordance with the present invention;

FIG. 35 is a diagram of another example of a generalized operation setlimit table in accordance with the present invention;

FIG. 36 is a diagram of an example of operation sequencing based on anoperation set limit table in accordance with the present invention;

FIG. 37 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 38 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 39 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIG. 40 is a schematic block diagram of another example of an assetmodification module modifying an asset in accordance with the presentinvention;

FIGS. 41-42 are a diagram of another example of an operation set limittable in accordance with the present invention;

FIG. 43 is a diagram of an example of modifying an asset based on thelimit table of FIGS. 41-42 in accordance with the present invention;

FIG. 44 is a diagram of another example of modifying an asset based onthe limit table of FIGS. 41-42 in accordance with the present invention;

FIG. 45 is a diagram of another example of modifying an asset based onthe limit table of FIGS. 41-42 in accordance with the present invention;

FIG. 46 is a diagram of another example of modifying an asset based onthe limit table of FIGS. 41-42 in accordance with the present invention;

FIG. 47 is a diagram of another example of modifying an asset based onthe limit table of FIGS. 41-42 in accordance with the present invention;

FIG. 48 is a diagram of an example of interoperations of multipleoperation set limit tables in accordance with the present invention;

FIG. 49 is a diagram of an example of operation of an operation setlimit table in accordance with the present invention;

FIG. 50 is a diagram of another example of operation of an operation setlimit table in accordance with the present invention;

FIG. 51 is a diagram of another example of operation of an operation setlimit table in accordance with the present invention;

FIG. 52 is a diagram of another example of operation of an operation setlimit table in accordance with the present invention;

FIG. 53 is a diagram of another example of an operation set limit tablein accordance with the present invention;

FIG. 54 is a diagram of an example of building an operation set limittable in accordance with the present invention;

FIG. 55 is a diagram of an example of historical evaluation data forbuilding an operation set limit table in accordance with the presentinvention;

FIG. 56 is a schematic block diagram of an embodiment of an evaluationfilter of an asset modification module in accordance with the presentinvention;

FIG. 57 is a diagram of an example of a reference pattern in accordancewith the present invention;

FIG. 58 is a diagram of an example of a reference pattern in accordancewith the present invention;

FIG. 59 is a schematic block diagram of an example of operation of anevaluation filter in accordance with the present invention;

FIG. 60 is a diagram of an example output of an evaluation filter inaccordance with the present invention;

FIGS. 61-63 are a logic diagram of a method for building a limit tablein accordance with the present invention;

FIG. 64 is a logic diagram of another method for building a limit tablein accordance with the present invention;

FIG. 65 is a schematic block diagram of an example of an assembly lineof building a product in accordance with the present invention;

FIG. 66 is a diagram of an example of limit tables for inventorymanagement of an assembly line of building a product in accordance withthe present invention;

FIG. 67 is a diagram of an example of limit tables for inventorymanagement of an assembly line of building a product in accordance withthe present invention;

FIG. 68 is a diagram of an example of inventory management of anassembly line of building a product in accordance with the presentinvention;

FIG. 69 is a diagram of an example of graphical user interface inaccordance with the present invention;

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a datacommunication system that includes a plurality of user devices 10-12, aplurality of service provider devices 14-16, a plurality of evaluationcontent sources 18-20, and one or more networks 22 (e.g., one or morelocal area networks (LAN), one or more wireless LANs (WLAN), one or morewide area networks (WAN), the Internet, etc.). Each of the user devices10-12 includes one or more network interfaces 24 (e.g., LAN, WLAN, WAN,Internet, etc.), one or more processing modules 26, and memory 28. Eachof the service provider devices 14-16 includes one or more networkinterfaces 24, one or more processing modules 26, and memory 28. The oneor more processing modules 26 of the user devices 10-12 and/or theservice provider devices 14-16 implement one or more asset modificationmodules 30.

In an example of operation, a user device 10-12 has a portfolio ofassets 32, a collection of operation set limit tables 34, and a pool ofoperations 36. The assets 32 may be tangible property (e.g., realestate, inventory, energy (e.g., gas, electric, etc.), financial capital(e.g., money, stock, bonds, precious metals, etc.), etc.), intangibleassets (e.g., patents, copyrights, trademarks, good will, trade secrets,etc.), intelligence information (e.g., personal data, person of interestdata, weather data, sports data, traffic data, competitor information,etc.), a rented asset, and/or a disposable asset (e.g., tickets). Eachlimit table 34 includes entries, where an entry includes identity of oneor more evaluation data to monitor, trigger indicators, de-triggerindicators, activation indicators, de-activations indicators, identityof one or more operations of the pool of operations 36, operational dataindicators, status information, etc. The pool of operations 36 includesoperations regarding modification of an asset, where modificationincludes increase amount of an asset, decrease amount of an asset,dispose of some or all of an asset, use some or all of the asset,transfer some or all of the asset, assign some or all of the asset,adjust sales prices of an asset, adjust anticipated purchase price of anasset, adjust purchasing procedures of an asset, buy more of an asset,sell some or all of an asset, etc. Other operations of the pool ofoperations 36 include compiling evaluation data, evaluating evaluationdata, trend recognition, pattern recognition, data extrapolation, etc.

For a specific asset, the asset modification module 30 uses an operationset limit table to manage the modification of the asset. The operationset limit table may be the only one for this asset or may be selectedfrom a plurality of operation set limit tables 34 for this asset. Theselection of an operation set limit table may be automatic based on userpreferences (e.g., a conservative approach, an aggressive approach,modification thresholds for the asset (e.g., lower limit on how theasset might decrease), evaluation data of preference, how the asset ismodifying during a given period of time, etc.) or selected via a userinput.

As a specific example, assume that the asset is a stock. In thisspecific example, the limit table includes entries on when to buy moreof the stock and how much to buy based on various conditions, trends,patterns, limits, and/or other factors of the evaluation data (e.g.,line price data, candlestick data, average position price, simple movingaverage, exponential moving average, Bollinger bands, money flow, MACD,parabolic SAR, rate of change, relative strength, slow Stochastic, fastStochastic, volume, volume+moving average, William % R, etc.). The limittable also includes entries on when to sell some or all of the stockbased on various conditions, trends, patterns, limits, and/or otherfactors of the evaluation data. The limit table further includes entrieson when to “open” the stock for modification and when to “close” thestock modification.

As another specific example, assume that the asset is a component of aproduction product. In this specific example, the selected limit tablegenerally includes entries to manage maintaining inventory of thecomponent at a desired level (e.g., just-in-time, having a surplus,etc.) for manufacturing the product based on various conditions, trends,patterns, limits, and/or other factors of the evaluation data (e.g.,component pricing, component availability, political issues effectingavailability of the component, sub-component material pricing,sub-component material availability, shipping, import/export factors,assembly requirements, labor issues, weather, supply-demand information,market information for the product, etc.). For instance, the limit tablemay include an entry to determine when to buy the component, an entry todetermine the per purchase quantity of the component, an entry todetermine the purchase price of the component, an entry to select one ormore vendors, an entry to determine a desired inventory level, etc.

As another specific example, assume that the asset is season tickets toa sporting event. In this specific example, the selected limit tablegenerally includes entries to manage, on a per event basis, the sale oftickets, use of the tickets, donation of the tickets, and/or transfer ofthe tickets to family or friends based on various conditions, trends,patterns, limits, and/or other factors of the evaluation data (e.g.,season ticket holder's schedule, weather, opponent's fan following,current success of home team, standings, time of season, playoffscenarios, season ticket holder's desired level of attendance, otherticket sales quantity for an event, other ticket sales pricing for theevent, etc.). For instance, the limit table may include an entry to setthe sales price above face value, an entry to set the sales price belowface value, an entry to determine which events to sell the tickets, anentry to determine which events to use the tickets, etc.

As another specific example, assume that the asset is intelligence dataregarding a particular person. In this specific example, the selectedlimit table generally includes entries to manage data regarding anindividual based on various conditions, trends, patterns, limits, and/orother factors of the evaluation data (e.g., person's financialinformation, person's press clippings, person's family information,person's education information, person's employment information, numberof inventions, number of published papers, purchase history, etc.). Forinstance, the limit table may include an entry to determine the person'sshopping preferences, an entry to determine the person's likes, an entryto determine the person's dislikes, an entry to determine the person'sprofession success, an entry to determine the person's potential foremployment viability, etc.

In another example of operation, a service provider device 14-16functions similarly to a user device 10-12, but does so on behalf ofclients. As such, for each client, the service provider devices 14-16functions to manage the client's assets in similar manner as the userdevice 10-12. In this regard, for each client, the service providerdevice 14-16 stores a portfolio of assets 32, a collection of operationset limit tables 34, and a pool of operations 36.

FIG. 1A is a schematic block diagram of an embodiment of a device 11(e.g., user device 10 and/or service provider device 14) that includesone or more network interfaces 24, one or more processing modules 26,and memory 28. The processing module(s) 26 implement one or more assetmodification modules 30. The memory 28 stores a plurality of limittables 38-40, operational data 42-44 regarding assets (e.g., tangibleassets, intangible assets, financial capital, intelligence information,rented assets, disposable assets, etc.), and operation instruction sets46-48.

In an example of operation, the processing module 26 selects an asset tomodify from a portfolio of assets. The processing module 26 identifieswhich of the plurality of limit tables 38-40 correspond to the selectedasset and selects one or more of the limit tables. The processing moduleinterprets the appropriate limit table, or tables, to identifytime-varying and time-sensitive data regarding the asset and one or morecorresponding operation sets. The processing module receives thetime-varying and time-sensitive data from a local area network or a widearea network through the network interface 24. The time-varying andtime-sensitive data is data from one or more sources (e.g., subscriptionbased data providers, free data providers, vendors, suppliers, weather,government, etc.) that varies over time and, for the purposes of acurrent asset modification, is only useful for a given period of time(e.g., minutes, days, weeks, months, years).

The processing module evaluates the time-varying and time-sensitive databased on evaluation criteria contained in the one or more limit tables38-40. When the evaluation is favorable, the one or more operationinstruction sets 46-48 are triggered (e.g., one or more operations isselected, is retrieved from memory, is loaded into the processingmodule, is readied for execution, etc.). With an operation triggered,the processing module further evaluates the time-varying andtime-sensitive data based on correlated evaluation criteria of the limittables 38-40 to determine whether to activate the operation instructionsets 46-48. When activated, the processing module executes the operationon the operational data 42-44 to modify the asset.

FIG. 1B is a schematic block diagram of an embodiment of a computerreadable storage medium 50 coupled to device 11 (e.g., the user device10 and/or the service provider device 14). The computer readable storagemedium includes a plurality of storage sections 52 that store dataand/or operational instructions. A storage section 52 includes one ormore byte-addressable, word-addressable, multiple word-addressable, orother sized-addressable lines such that the storage section is capableof storing bytes to Giga-bytes of data and/or operational instructions.The computer readable storage medium 50 may be one or more memorydevices, where a memory device is a disk, a memory of a download source,a flash memory, a USB drive, an external hard drive, a memory of acomputer, etc.

In an example of operation, the computer readable storage medium 50, ora portion thereof, contains five storage sections 52; each of whichstoring operational instructions that, when executed by the processingmodule 26, causes the processing module to perform one or morefunctions, tasks, sub-routines, algorithms, etc. A first storage sectionstores operational instructions 54 that cause the processing module 26to obtain one or more limit tables regarding an asset. A second storagesection stores operational instructions 54 that cause the processingmodule 26 to identify time-varying and time-sensitive data from the oneor more limit tables. A third storage section stores operationalinstructions 54 that cause the processing module 26 to analyze thetime-varying and time-sensitive data based on evaluation criteria fromthe one or more limit tables. A fourth storage section 52 storesoperational instructions 54 that, when the analyzing the time-varyingand time-sensitive data is favorable, causes the processing module 26 totrigger one or more operations of a set of operations corresponding to aparticular criteria.

With one or more operations triggered, a fifth storage section 54 storesoperational instructions 54 that causes the processing module 26 tofurther analyze the time-varying and time-sensitive data based oncorrelated evaluation criteria of the evaluation criteria. When thefurther analyzing the time-varying and time-sensitive data in view ofthe correlated particular criteria is favorable, the processing module26 activates the one or more operations to execute a correspondingfunction on operational data corresponding to the asset such that theasset is modified.

FIG. 2 is a schematic block diagram of a functional embodiment of anasset modification module 30. The asset modification module 30 manages aportfolio of assets 32 using operation sets 56. For instance, for aparticular asset (e.g., tangible property, financial capital, intangibleassets, intelligence information, a rented asset, and/or a disposableasset), the asset modification module 30 selects a limit table from oneor more limit tables for the asset. The limit table identifies anoperation set 56 (e.g., a set of operations used to modify the asset inaccordance with limits, conditions, parameters, etc., listed in thelimit table. Note that one or more operations for modifying an asset maybe in one or more operation sets 56.

As a specific example, assume that asset #1 is the selected asset andasset #1 operation (op) set #1 is identified via selection of thecorresponding limit table 1-1. A first operation of the set ofoperations may be to “open” asset #1 for modification. This may be doneat the selection of limit table 1-1 or it may be an operation within thelimit table based on a condition of one or more evaluation data. Oncethe asset is open, an operation is evoked based on an entry of the limittable based on conditions of one or more evaluation data. For example,an entry may evoke an operation to buy a particular quantity of asset #1(e.g., component of a manufactured product) at a best current price whena particular inventory level is reached. Another entry may evoke anoperation to buy a particular quantity of the asset when a particularprice is reached. Another entry may evoke an operation to determine whento buy a particular quantity when the evaluation data indicates that thesupply of the asset may drop and the price may go up. Another entry mayevoke an operation to track use of the asset and/or potential future useof the asset. Another entry may evoke an operation to close the assetwhen a certain level of inventory is reached. These are but a fewexamples of operations that may be evoked by entries in a limit table tomanage the inventory of a component or of any other limit table.

When the asset is closed, the asset modification module returns it tothe portfolio of assets (i.e., is no longer actively in a modificationmode of the asset). The asset modification module 30 may modify oneasset at a time or a plurality of assets concurrently. If the assetmodification module 30 is modifying multiple assets, each asset istreated separately with respect to selection of a limit table and thecorresponding operations as well as execution of the correspondingoperations based on the limit table.

FIG. 3 is a diagram of an example of modifying an asset based onevaluation data 58 (D1, D2, and D3). Each of the evaluation data 58 istime varying and time sensitive, wherein the duration of variation mayvary by the second, by the minute, by the hour, by the day, by the week,by the month, and/or by the year. For example, an evaluation data 58 maybe a stock price, which varies at fractions of a second. As anotherexample, an evaluation data 58 may be weather conditions, which variesby the minute, by the hour, by the day, by the week, by the month, andby the year, where its variation rate of interest depends how theweather data is being evaluated. For instance, if the weather data isbeing used as factor in determining the long-term availability of aresource (e.g., natural, element, component, etc.), then the variationrate may be months and/or years. Alternatively, if the weather data isbeing used as a factor in determining whether to sell tickets fortonight's baseball game, then the variation rate may be minutes and/orhours. Other examples of evaluation data 58 are almost endless andcorrespond to data of interest regarding a particular asset. In thediagram, the horizontal axis represents time 60 (which may be measuredin seconds, minutes, hours, days, weeks, months, and/or years), thepositive vertical axis represents evaluation data variations 62, and thenegative vertical axis represents operation data of the asset 64.Operation data of the asset 64 may include quantity, price, dataoperands (e.g., opening price, baseline values, opening quantities,consumption quantities, etc.), data allotment (e.g., a percentage of thecurrent asset to expose to modification), asset information, etc.

In an example of operation, once the asset is open 76 for modification78, the evaluation data 58 is monitored to detect a triggering condition66 (e.g., price increase to a certain level, price decrease to a certainlevel, supply-demand information at a certain level, combination ofevaluation data trend and/or pattern detection, etc.). When a triggeringcondition 66 is met, the asset modification module then determineswhether an activate condition 68 is met, which may be at the samethreshold as the triggering condition 66 or at a different threshold.When the activation condition 68 is met, the corresponding operation 70is activated until a deactivation condition 72 is met or themodification 78 of the asset is closed 80.

In this example, operation 2 (O2) is triggered by the opening of theasset and is activated when evaluation data D2 and D3 become available.With operation 2 active, evaluation data D3 is monitored for aparticular condition to cause operation 2 to execute (e.g., buy whenprice is below $x.00). Alternatively, operation 2 may be continuallyexecuted based on evaluation data D3 (e.g., compare D3 with stored datato determine a variance). As operation 2 is executed, the operation dataof the asset 64 is modified. Operation 2 is deactivated when evaluationdata D3 is no longer available and is de-triggered 74 when themodification 78 of the asset is closed 80.

As is further included in this example, when evaluation data D1 beginsto rise, operation 1 is triggered 66 and remains in the triggered state(e.g., grey shaded area) until it rises further. At this point,operation 1 is activated 68 and is executed when a particular conditionof evaluation data D1 is met or is continually executed using evaluationdata D1. Operation 3 is triggered 66, activated 68, deactivated 72, andde-triggered 74 based on factors, trends, and/or patterns of D1-D3.

FIG. 3A is a diagram of another example of modifying an asset based onevaluation data 58 (D1, D2, and D3). Each of the evaluation data 58 istime varying and time sensitive. In the diagram, the horizontal axisrepresents time 60 (which may be measured in seconds, minutes, hours,days, weeks, months, and/or years), the positive vertical axisrepresents evaluation data variations 62, and the negative vertical axisrepresents operation data of the asset 64.

In an example of operation, once the asset is open 76 for modification78, the evaluation data 58 is monitored to detect a triggering condition66. When a triggering condition 66 is met, the asset modification modulethen determines whether an activate condition 68 is met, which may be atthe same threshold as the triggering condition 66 or at a differentthreshold. When the activation condition 68 is met, the correspondingoperation 70 is activated and executed. Once executed, the operation 70is deactivated 72 and placed in the triggered state again. When theactivation condition 68 is again met, the operation 70 is activated andexecuted; once executed it is again deactivated 72 and placed in thetriggered state. This cycle of activate, execute, and re-triggercontinues until a de-trigger condition 74 is met. For example, if theasset is a stock and the evaluation data 58 continues to show favorableconditions for purchasing additional shares of the stock, then theoperation of purchasing shares will be repeated each time the activationcondition 68 is met.

FIG. 4 is a schematic block diagram of an example of an assetmodification module 30 modifying an asset 86. In this example, the assetmodification module 86 selects the asset 86 to modify and a limit table88. In addition, the asset modification module 30 receives evaluationdata 90, which may be received from one or more evaluation data contentsources. The limit table 88 identifies a corresponding operation set 92(e.g., the operations identified in the limit table 88).

In an example of operation, the asset module 30 monitors the evaluationdata 90 in accordance with entries of the limit table 88 for acondition, pattern, trend, peak value, valley value, etc. to be met totrigger an operation. When an operation is triggered, the assetmodification module 30 retrieves it from the corresponding operation set92 and sets it in the triggered state. When an activation condition,pattern, trend, peak value, valley value, etc. is met, the assetmodification module 30 changes the operation to an active state andexecutes the operation when appropriate based on the evaluation dataanalysis. This process continues until the asset modification module 30closes modification of the asset and outputs a modified asset 94.

FIG. 5 is a logic diagram of a method for modifying an asset that isexecutable by the asset modification module of a device 11. The methodbegins at step 96 the module selects an asset from a portfolio ofassets. Recall that an asset may be tangible property (e.g., realestate, inventory, energy (e.g., gas, electric, etc.), financial capital(e.g., money, stock, bonds, precious metals, etc.), etc.), intangibleassets (e.g., patents, copyrights, trademarks, good will, trade secrets,etc.), intelligence information (e.g., personal data, person of interestdata, weather data, sports data, traffic data, competitor information,etc.), a rented asset, and/or a disposable asset (e.g., tickets). Theselection of an asset may be based on factors associated with the asset,where the factors includes a type of asset, modification timing (e.g.,time of day, inventory depletion, etc.), evaluation data sources ofinterest availability, evaluation data analysis (e.g., pattern mapping,trend detection, value thresholds, comparative analysis, etc.).

The method continues at step 98 where the module selects or creates anoperation set limit table for the asset 98 to be modified. For example,a plurality of operation set limit tables may exist for a particularasset. Each of the operation set limit tables is developed based ondifferent attributes (e.g., risk level, evaluation data relevancy, assetmodification philosophy, reliability level (e.g., proven, unproven,works some times, etc.), favorable evaluation data patterns and/ortrends, performance information, etc.). Accordingly, for this example,the operation set limit table is selected based on its attributesaligning with the desires of the user. Alternatively, the assetmodification module may create an operation set limit table, which isdiscussed with reference to one or more subsequent figures.

The method continues at step 100 where the module analyzes time-varyingand time-sensitive data based on evaluation criteria of interestidentified by the operation set limit table (examples are discussed withreference to one or more subsequent figures). The method continues atstep 102 where the module determines whether analysis of thetime-varying and time-sensitive data in view of a particular criteria ofthe evaluation criteria is favorable for triggering an operation. Ifnot, the process continues at step 104 where the module determineswhether modification of the asset is being closed (e.g., user initiated,evaluation data determined, limit table indicator, etc.). If themodification is being closed, the method continues at step 106 where theasset modification module outputs a modified asset. Note that the assetmay not be modified if the modification process was closed before anoperation was executed. If the modification is not being closed, themethod repeats at the analyze evaluation data step 100.

If the time-varying and time-sensitive data in view of a particularcriteria of the evaluation criteria is favorable for triggering theoperation, the operation is triggered and the method continues at step108 where the module further analyzes the time-varying andtime-sensitive data based on correlated evaluation criteria of theevaluation criteria to determine whether the operation is to beactivated. Note that the evaluation criteria provides one or more of adesired trend, a desired pattern, a desired slope, a desired value, adesired indicator, a desired threshold, a desired deviation over time,etc. Further note that the particular criteria and the correlatedcriteria may be the same desired evaluation criteria such that anoperation is triggered and activated substantially simultaneously or maybe different desired evaluation criteria such that the operation istriggered at one level of the desired evaluation criteria and activatedat another level.

If the operation is not to be activated, the method continues at step110 where the module determines whether the evaluation data analysisindicates that the operation is to be detriggered. If not, the methodrepeats in a loop for this operation between the activating step 108 andthe detriggering 110. The method also branches back to the analyzeevaluation data step 100 for another operation. Accordingly, the modulemay be executing the method of FIG. 5 for multiple operations in view ofthe same or different evaluation data and/or criteria to modify theselected asset.

If the operation is activated, the method continues at step 111 wherethe module executes the operation in accordance with operation dataindicators of the operation set limit table. The method continues atstep 112 where the module determines whether the operation is to bedeactivated. The operation may be deactivated upon execution of theoperation or based upon an indicator of the evaluation data analysisindicating deactivation of the operation. If not, the method repeats atthe operation execution step 111. The method also branches back to theanalyze evaluation data step 100 for another operation. If the operationis being deactivated, the method continues at the detriggerdetermination step 110. Note that when the operation is deactivated, itis placed back in the triggered state unless otherwise indicated in theoperation set limit table.

If an indicator of the evaluation data analysis indicates detriggeringthe operation, the method continues at step 114 where the moduledetermines whether the modification of the asset is to be closed. If themodification is to be closed, the method continues at step 116 where theasset modification module outputs a modified asset. If the modificationis not being closed, the method repeats, for this operation, at thetrigger determination step 102 and repeats at the evaluation data step100 for another operation.

FIG. 5A is a logic diagram of a method for modifying an asset that isexecutable by the asset modification module within the processing moduleof the user and/or service provider device. The method begins at step118 where the module obtains, by selecting from stored limit tables orcreating a new one) one or more limit tables regarding an asset to bemodified. The method continues at step 120 where the asset modificationmodule identifies time-varying and time-sensitive data from the one ormore limit tables regarding the asset to be modified 120. Thetime-varying and time-sensitive data may be obtained from one or morewide area network (WAN), local area network (LAN) data sources (e.g.,news, weather, sports, credit tracking, criminal records, financialinformation, real estate information, data collection, marketinginformation, sales information, forecasting, vendor web pages,governmental, etc.) via network interface(s) on a free or subscriptionbasis.

The method continues at step 122 where the asset modification moduleanalyzes the time-varying and time-sensitive data based on evaluationcriteria from the one or more limit tables. In general, the analysis ofthe data is based on evaluation criteria that includes, but is notlimited to, detecting predictable patterns, trends, factors, values,slopes, deviations, indicators, thresholds, comparative analysis, etc.of the current evaluation data. For example, the analysis is based on aparticular one or more of the evaluation criteria such as a particularpattern mapping, a particular trend, a particular value, a particularthreshold, a particular comparative analysis, a particular slope, aparticular word content, a particular phrase content, a particular timestamp, a particular data of interest identifiers, etc. as is describedin greater detail with reference to one or more of FIGS. 7, 21, and 24.The analysis may further include a level of favorability (orunfavorability), which indicates how closely the detected patterns,trends, factors, values, etc. match past patterns, trends, factorvalues, etc. and the likelihood they will continue to match. The levelof favorability (or unfavorability) is a factor in determining thepredictability that the asset will be modified in a desired manner. Forexample, when the analysis yields patterns, trends, etc. that don'tparticularly match the predictable patterns, trends, etc., thefavorability may be indeterminate as is described in greater detail withreference to FIG. 54.

The method branches at step 124 based on whether analysis of step 122 isfavorable. When the analysis is unfavorable, the method continues atstep 125 where the asset modification module determines whether to closethe modification of the asset. If the asset modification is to beclosed, the method ends for modification of the selected asset. If themodification is not to be closed, the method repeats at step 120 wherethe asset modification module identifies new evaluation data (e.g., thetime-varying and time sensitive data) or uses the previously selectedevaluation data.

When the analysis of step 124 is favorable, the method continues at step126 where the asset modification module triggers one or more operationsof a set of operations, which is identified in the limit table(s). Themethod continues at step 128 where the asset modification module furtheranalyzing the evaluation data based on correlated evaluation criteria.In an example, the correlated evaluation criteria correlates to theparticular evaluation criteria, but a different desired level, value,threshold, etc.

The method branches at step 130 depending on whether the analysis ofstep 128 is favorable. When the analysis is unfavorable, the methodcontinues at step 131 where the asset modification module determineswhether to detrigger the operation. The asset modification module maydetrigger the operation when the analysis of the evaluation datacompares unfavorably to the particular evaluation criteria, a timeperiod elapses, as an auto-response to an unfavorable correlatedevaluation criteria analysis, etc. If the operation is not to bedetrigger, the method repeats at step 128. If the operation is to bedetrigger, the asset modification module detriggers it and the methodrepeats at step 125.

If the further analysis based on the correlated evaluation criteria isfavorable, the method continues at step 132 where the asset modificationmodule activates the operation is activated. The method continues atstep 133 where the asset modification module executes the operation onthe operational data corresponding to the asset to produce anintermediate modified asset. The execution of the operation may occursubstantially immediately after activation, after a predetermined periodof, after the evaluation data remains in a favorable relation to thecorrelated evaluation criteria for a given period of time, etc. Inanother example, the particular criteria and the correlated criteria maybe the same. In this case, step 126, where the operation is triggered,and step 132, where the operation is activated and executed are the sametime; thus, the operation is triggered, activated, and executed in thesame step.

The method continues at step 134 where the asset modification moduledetermines whether to deactivate the operation and place it in atriggered state. The asset modification module may deactivate theoperation as an auto-response to executing the operation, when theanalysis of the evaluation data in view of the correlated evaluationcriteria is unfavorable, after elapse of a time period, after theoperation has been executed a given number of times, etc. If theoperation is to be deactivated, the method continues at step 136 wherethe module deactivates the operation, places it back in the triggeredstate, and the method repeats at step 128. If the operation is not to bedeactivated, the method repeats at step 133. Note that the repeating ofexecution of the operation may be substantially continual or may includesome delay or hysteresis. Further note that the repeating of theexecution of the operation may produce a cumulative modification of theasset or a superseding modification of the asset.

FIG. 5B is a logic diagram of a method for modifying an asset that isexecutable by the asset modification module within the processing moduleof the user or service provider device. The method begins at step 138where the asset modification module selects an asset from a plurality ofassets. The method continues at step 140 where the asset modificationmodule identifies one or more limit tables of a plurality of limittables corresponding to the selected asset. The method continues at step142 where the module retrieves, via the network interface module,time-varying and time-sensitive data (e.g., evaluation data) based oninformation from the one or more limit tables.

The method continues at step 144 where the module identifies one or moreoperations of a plurality of operations based on evaluation of thetime-varying and time-sensitive data as indicated in the one or morelimit tables. The method continues at step 146 where the moduleretrieves operation data corresponding to the selected asset. Theoperational data may include the selected asset, an asset identifier(ID), information regarding the selected asset (e.g., quantity, value,use rate, etc.), and/or other data regarding the asset.

The method continues at step 148 where the module triggers the one ormore operations based on further evaluation of the time-varying andtime-sensitive data. The method continues at step 150 where the moduleexecutes the one or more operations on the operation data for theselected asset based on further evaluation of the time-varying andtime-sensitive data.

FIG. 6 is a schematic block diagram of an example of a set of operationsfor modifying an asset. Each of the operations may be an operationmodule (e.g., software and/or hardware) that includes a plurality ofinputs and one or more outputs. The inputs include operation dataindicator, or indicators, (ODI), evaluation data indicator, orindicators, (EDI), trigger indicator, or indicators, (TI), de-triggerindicator, or indicators, (DT), activate for execution indicator, orindicators, (EI), pause or deactivate indicator, or indicators, (PI),and/or data input (Din). The output includes a resultant output (Rout).

For an operation, the operation data indicator(s) provide indicationsregarding operation data of the asset. Such indicators may include anoperation data identifier (ID), a data allotment, and one or more dataoperands. For example, the operation data ID identifies the asset, ordata regarding the asset. As a more specific example, the operation dataID may identify a stock for trading, a person for collecting informationabout him or her, employment data of a person, a part number of acomponent, a region for weather information, sporting event tickets,etc.

The data allotment provides operational limits on the operation databeing modified. For example, the data allotment may indicate how much ofthe identified stock is to be executed upon by the operation, what yearsto obtain information regarding a person, certain employers of a personfor employment data, a quantity of an identified part, an amount of timefor collecting weather data of a region, dates for the sporting eventtickets, etc. Note that the data allotment may be predetermined via theoperation set limit table or it may be a calculation of the operationand/or another operation.

The one or more data operands, if included, provide operand informationregarding the operation data. For example, a data operand may be aninitial value of the operation data, baseline data for the operationdata, data conditions (e.g., limits, thresholds, rounding preferences,etc.) regarding the operation data, data formatting (e.g., units, numberof decimal places, date format, font, etc.), and/or calculationparameters (e.g., function to perform to determine baseline data,initial data, etc.). As a more specific example, the data operand mayindicate currency of the identified stock, date format for the years toobtain information regarding a person, certain employers of a person foremployment data, quantity units (e.g., per piece, per dozen, per lots of100, etc.) of an identified part, a start time for collecting weatherdata of the region, quantity of tickets for the specific dates regardingthe sporting event, etc.

Continuing with inputs for the operation, each of the evaluation dataindicator(s) (EDI) includes an evaluation data identifier (ID) and oneor more evaluation data factors. The evaluation data ID identifies oneor more evaluation data sources of interest for the particularoperation. For example, the evaluation data of interest for a stock maybe one or more of line price data, candlestick data, average positionprice, simple moving average, exponential moving average, Bollingerbands, money flow, MACD, parabolic SAR, rate of change, relativestrength, slow Stochastic, fast Stochastic, volume, volume+movingaverage, William % R, etc. As a further example, the evaluation data ofinterest for information regarding a person may be specific newspapers,specific magazines, credit rating information, criminal history,financial information, published patent applications, articlesauthorized by the person, etc. As another example, the evaluation dataof interest for employment data of a person may be tax returns,information regarding the employer, personnel files, etc. As yet anotherexample, the evaluation data of interest for weather of a particularregion may be local forecasts, national weather service advisories,video data, informant information, etc.

An evaluation data factor for evaluation data of interest includes oneor more of a time frame for the evaluation data (e.g., data of May 12,2012), source filtering information, evaluation data manipulationrequirements, etc. For example, the source filtering information may beused to weight information from one source as more relevant than fromanother source, to filter out information from a particular source, topass only information from one or more selected authors, etc. As anotherexample, the evaluation data manipulation requirements provideinformation regarding changing format of the evaluation data (e.g.,language translation, font, spacing, size, paragraph structure, tabularform, spreadsheet form, etc.); performing a function on the evaluationdata (e.g., a mathematical function (e.g., an average function, an RMSfunction, etc.), graphing, scaling, compressing, etc.); performing afunction of the evaluation data with other evaluation data (e.g.,comparing, adding, subtracting, multiplying, dividing, averaging,statistical analysis, etc.).

Continuing with inputs for the operation, the trigger indicator(s)indicate when the operation is to be triggered (e.g., awaken and readyfor execution) based on analysis of the identified evaluation data. Forexample, the trigger indicator may indicate that, when the evaluationdata reaches a certain value, the operation is to be triggered. Asanother example, the trigger indicator may indicate that, when theevaluation data exhibits a certain pattern and/or trend, the operationis to be triggered. As yet another example, the trigger indicator mayindicate that, when a combination of evaluation data exhibits a certainpattern and/or trend, the operation is to be triggered. The de-triggerindicator(s) indicate when the operation is to be detriggered (e.g., putinto a sleep mode or disabled).

Continuing with inputs for the operation, the activate for executionindicator(s) indicate when a triggered operation is to be activated(e.g., enabled for execution) based on analysis of the identifiedevaluation data. For example, the activate for execution indicator mayindicate that, when the evaluation data reaches a certain value, thetriggered operation is to be activated. As another example, the activatefor execution indicator may indicate that, when the evaluation dataexhibits a certain pattern and/or trend, the triggered operation is tobe activated. As yet another example, the activate for executionindicator may indicate that, when a combination evaluation data exhibitsa certain pattern and/or trend, the operation is to be activated. Thede-activate for execution indicator(s) indicate when the operation is tobe deactivated (e.g., put back into the triggered mode).

FIG. 7 is a schematic block diagram of an embodiment of assetmodification operation module 152 that includes a data filter 154, awake-up/sleep module 156, an execute activate/deactivate module 158, andan execution module 160. The operation module 152 may be implemented asa software module executable by a processing module, may be implementedas a hard coded module, and/or may be implemented as a firmware module(e.g., a combination of software and hardware).

In an example of operation, the data filter 154 filters one or moreevaluation data 162 based on one or more evaluation data indicators 164to produce one or more filtered evaluation data outputs. Since theevaluation data 162 is time-varying and time-sensitive, the one or morefiltered evaluation data outputs are a continuous stream of data, acontinuous stream of data samples, and/or a combination thereof. Notethat the evaluation data 162 may include an output of one or more otheroperations. Further note that the evaluation data 162 may include theoutput resultant 166 of the operation module 152.

The wake-up/sleep module 156 (which could also be called atrigger/detrigger module) receives the one or more filtered evaluationdata outputs and analyzes them in view of the one or more triggerindicators 168. When the analysis is favorable, the wake-up/sleep module156 wakes up, or triggers, the operation module 152. A favorableanalysis may result by detecting a trigger signal; by detecting thefiltered evaluation data includes a data aspect (e.g., value, trend,pattern, slope, word content, phrase content, time stamp, data ofinterest identifier, etc.) that compares favorably to a triggerindicator 168; by detecting a desired result of another operation;and/or by detecting one or more other factors within the filteredevaluation data.

When the operation is triggered (e.g., awake), the executeactivate/deactivate module 158 is engaged to analyze the one or morefiltered evaluation data outputs in view of one or more execute (oractivate) indicators 170. When the analysis is favorable, the executeactivate/deactivate module 158 enables, or activates, the operationmodule 152. A favorable analysis may result by detecting an activatesignal; by detecting the wake up signal (e.g., an execute indicator isthe same as a trigger indicator); by detecting the filtered evaluationdata includes a data aspect (e.g., value, trend, pattern, slope, wordcontent, phrase content, time stamp, data of interest identifier, etc.)that compares favorably to an execute (or activate) indicator; bydetecting a desired result of another operation; and/or by detecting oneor more other factors within the filtered evaluation data.

With the operation active, the execution module 160 is enabled toperform its operation on the operation data 174 and/or on the evaluationdata 162 in view of the operation data indicators 172. For example, theoperation may be a logic function, a mathematical function, analgorithm, and/or an operational instruction. As a more specificexample, the operation is an algorithm to purchase stock, which isidentified by the operation data 174 and/or the operation dataindicators 172. As another more specific example, the operation is analgorithm to sell sporting event tickets, which are identified by theoperation data and/or the operation data indicators. As yet another morespecific example, the operation is an algorithm to update a file on aperson with newly found evaluation data. As a further more specificexample, the operation is an algorithm to determine when to place apurchase order for a component of a manufactured product. As an evenfurther more specific example, the operation is an algorithm to generatean alarm when the national weather service has issued a severe stormwarning for a region. As a still further more specific example, theoperation is a function to change the formatting of a selectedevaluation data.

When the operation is active, the execute activate/deactivate module 158is analyzing the one or more filtered evaluation data outputs in view ofone or more pause (or deactivate) indicators 176 to determine whether todeactivate, or disable, the operation module 152. When the analysis isfavorable, the execute activate/deactivate module 158 disables, ordeactivates, the operation module 152, which places the operation module152 back in the triggered state. A favorable analysis may result bydetecting a deactivation signal; by detecting completion of theexecution module performing its operation; by detecting the filteredevaluation data includes a data aspect (e.g., value, trend, pattern,slope, word content, phrase content, time stamp, data of interestidentifier, etc.) that compares favorably to a pause (or deactivate)indicator 176; by detecting a desired result of another operation;and/or by detecting one or more other factors within the filteredevaluation data.

With the operation module 152 back in the triggered state, the executeactivate/deactivate module 158 analyzes the one or more filteredevaluation data outputs in view of one or more execute (or activate)indicators 170 to determine if the operation module 152 should beactivated. Also, the wake-up/sleep module 156 analyzes the one or morefiltered evaluation data outputs in view of the one or more detriggerindicators 178. When the analysis is favorable, the wake-up/sleep module156 places the operation module 152 in a sleep, or detriggered state. Afavorable analysis may result by detecting a detrigger signal; bydetecting the filtered evaluation data includes a data aspect (e.g.,value, trend, pattern, slope, word content, phrase content, time stamp,data of interest identifier, etc.) that compares favorably to adetrigger indicator 178; by detecting a desired result of anotheroperation; and/or by detecting one or more other factors within thefiltered evaluation data.

FIG. 8 is a schematic block diagram of another embodiment of assetmodification operation module 152 that includes a data filter 154, awake-up/sleep module 156, an execute activate/deactivate module 158, anda plurality of execution modules 160. The operation module 152 may beimplemented as a software module executable by a processing module, maybe implemented as a hard coded module, and/or may be implemented as afirmware module (e.g., a combination of software and hardware).

In an example of operation, the data filter 154 and the wake-sleepmodule 156 function as discussed with reference to FIG. 7. The executeactivate/deactivate module 158 functions similarly to the executeactivate/deactivate module 158 of FIG. 7 with the additional function ofselecting one or more of the plurality of execution modules 160. Theexecute activate/deactivate module 158 selects an execution module 160,or multiple execution modules 160, in accordance with information withinthe execute (or activate) indicators 170. For example, if the indicatorsindicate purchasing a stock, the execution module 160 that performs thealgorithm to purchase a stock is selected. If, however, the indicatorsindicate selling a stock, the execution module 160 that performs thealgorithm to sell a stock is selected.

FIG. 8A is a schematic block diagram of an embodiment of evaluation datafilter 154 that includes an indicator processing module 180, a pluralityof data manipulation modules 182-184, a plurality of recognition filters186-188, and an output module 190. The output module 190 includes aplurality of functional blocks 192-198 and a programmable switchingnetwork 200-204. The functional blocks include, but are not limited to,an analyzer 192, a comparator 194, a compiler 196, a data processingmodule 198, etc. The programmable switching network includes an inputswitch module 200, a plurality of switches (SW) 202, and an outputswitch module 204. Note that the evaluation filter 154 may beimplemented as a software module executable by a processing module, maybe implemented as a hard coded module, and/or may be implemented as afirmware module (e.g., a combination of software and hardware).

In an example of operation, the indicator processing module 180 receivesthe evaluation data indicator(s) 206 and may further receiveconfiguration information for a corresponding limit table. From theevaluation data indicator(s) 206 and/or the configuration information,the indicatory processing module generates control signals 208. Forinstance, the indicator processing module 180 generates control signals206 to enable one or more of the data manipulation modules 182-184 andto configure, if needed, the enable data manipulation module(s) 182-184.For example, an evaluation data indicator 206 identifies a particularevaluation data 210 and, based on the identity, the indicator processingmodule 180 assigns a data manipulation module 182-184 to the particularevaluation data 210. In addition, the indicator processing module 180may generate a configuration control signal 208 to configure the datamanipulation module 182-184 to compress, scale, buffer, transformformat, etc. of the particular evaluation data 210. Alternatively, theindicator processing module 180 may generate a control signal 206 tobypass a data manipulation module 182-184 such that evaluation data 210is provided directly to a recognition filter 186-188.

The indicator processing module 180 also generates control signals 208to enable one or more of the recognition filters 186-188 and may furthergenerate control signals 208 to configure filtering of an enabledrecognition filter 186-188. For example, at some of the recognitionfilters 186-188 have different and fixed filtering functions. An exampleof fixed filtering functions includes, for a particular publication asthe evaluation data 210, passing articles regarding a particularsubject, passing articles written by a particular author, passingarticles written in a particular time frame, etc. Another example offixed filter functions includes, for a particular evaluation data 210,passing the evaluation data 210 during a particular time window (e.g.,from 9 AM-4 PM eastern time). A further example of fixed functionfunctions includes a first filter function of passing data regarding aparticular subject (e.g., a component for a manufactured product,information regarding a person, weather, traffic, a sporting event,etc.) and a second filter function of passing a subset of data based onone or more of the source of the data, timeliness of the data (e.g., ifnot current, which is a relative term, the data is filtered out), aparticular geographic region, etc.

The indicator processing module 180 further generates control signals208 to configure the output module 190. For example, the output module190 may be configured to output filtered evaluation data without furtherprocessing. As another example, one or more of the functional blocks192-198 of the output module 190 may be enabled to process filteredevaluation data. As a specific example, the analyzer 192 may be enabledto analyze the filtered evaluation data to extract, highlight, etc.certain aspects of it. As another specific example, the comparator 194may be enabled to compare two or more filtered evaluation data forredundancy, relevancy, timeliness, etc. and to output one of thecompared evaluation data. As a further specific example, the compiler196 may be enabled to compile two or more filtered evaluation data intocompiled evaluation data. As a further specific example, the dataprocessing module 198 may be enabled and configured to further processthe evaluation data. Such further processing may include compression,scaling, format transformation, combining multiple evaluation data intoa combined evaluation data (e.g., multiple weather reports fromdifferent sources into a single weather report), sort evaluation data,de-duplicate evaluation data, prioritize evaluation data, etc.

As yet another example of configuring the output module 190, multiplefunctional blocks 192-198 may be enabled such that an output of onefunctional block provides an input to another functional block. As afurther example of configuring the output module 190, a functional blockmay be configured to provide a loop function for a certain number ofloops and/or until a condition is met (e.g., the output of a functionalblock is fed back as the input of the functional block). Note that theinput switch module 200-204 includes sufficient buffering to temporarilystore filtered evaluation data.

FIG. 9 is a diagram of an example of an operation set limit table 212that includes a plurality of columns and one or more rows. The columnscorrespond to particular aspects of the operation set limit table 212such as sequence (ordering) 214, operation identifier (ID) 216, one ormore trigger indicators 218, one or more detrigger indicators 220,evaluation data indicators 224, execute (or activate) indicators 226,pause (or deactivate) indicators 228, operational data indicators 230,current operation status 232, and execution module selection 234. Notethat an operation limit table may include more or less columns (i.e.,aspects). For example, the execution module selection 234 may beomitted. As another example, the sequence column 214 may be omitted. Asyet another example, a column or aspect may be added to indicateconfiguration information for the evaluation data filter.

Each row of the operation set limit table 212 corresponds to anidentified operation and its operating conditions. For example, a firstrow below the header includes an entry in the sequence (ordering) field214 of 0 (1,x) and an entry in the operation ID 216 field of “A”, whichidentifies operation module A. In this example, the 0 represents thatoperation A is an initializing operation to begin modifying a particularasset. For example, operation A may be detecting an initialize assetmodification signal; may be detecting a certain condition of evaluationdata, etc. The (1,x) indicates the next sequence numbers that the assetmodification process may transition to after successful execution of anoperation having a sequence number of 0. In this example, operationshaving a sequence number of 1 or a sequence number of x are the nextpossible operations of the asset modification process.

Continuing with the example for operation “A”, the remaining fieldsincludes entries for triggering indicator(s) 218, detriggeringindicator(s) 220, evaluation data indicators 224 (which includesevaluation data ID 236 and may further include evaluation data factors238), execute (or activate) indicator(s) 226, pause (or deactivate)indicator(s) 228, operational data indicators 230 (which includesoperational data ID 240, data allotment 242, and/or data operands 244),current operation status 232, and execution module selection 234. Thecurrent operation status 232 includes whether the operation is waiting,in execution, completed, etc. The execution module selection 234 is usedto select an execution module of the operation module if it includesmore than one execution module.

The second row of the table includes an entry in the sequence (ordering)field 214 of 0 (1,x) and an entry in the operation ID field of “B”,which identifies operation module B. In this example, the 0 representsthat operation B is an initializing operation to begin modifying aparticular asset. In this example, operations A and B are eachinitializing operations and either one of the operations may be executedto transition the asset modification process to the next sequence. Theremaining rows have information regarding other operations for the assetmodification process, with one of them being a closing operation of theasset modification process.

FIGS. 10A-10C are diagrams of an example of asset modification. FIG. 10Aillustrates the first two columns of an operation set limit table (i.e.,sequence (ordering) 214 and operation ID 216); FIG. 10B illustrates thesequence transitions for operation set limit table; and FIG. 10Cillustrates the states of the operations for the sequencing of theoperation set limit table. In this example, the operation set limittable includes x number of sequences 246 and identifies Z operations. Atthe opening of the asset modification process of the operation set limittable, it starts in sequence state 0 and ends in sequence state x.

In the sequence 0 state, operations A and B are executable 250 (i.e.,can be triggered, activated, and/or executed), operations C, D, and Zare triggerable 248 (e.g., if the asset modification process transitionsout of sequence 0, these are the operations that may be executable), andoperations E-N are inactive 252 (e.g., are not executable when the assetmodification process transitions out of sequence 0). When operation A orB is successfully executed, the asset modification is process is in atransitional state (e.g., it can transition to sequence 1 or to sequencex). In this transitional state, operations C, D, and Z become executable250.

When a triggering condition for operation C or D occurs, the assetmodification process transitions from sequence 0 to sequence 1. In thissequence state 246, operations C and D are executable 250, operations E,F, G, and H are triggerable 248, and operations A, B, and I-Z areinactive 252. The asset modification process remains in sequence state 1as long as operation C or D is triggered and can execute operation C orD as often as indicated by the other field entries for the operation asset in the operation set limit table (e.g., the various indicators).When operations C and D become detriggered, the asset modificationprocess is again in a transitional state. From sequence 1, the assetmodification process may be transition to sequence 2 or to sequence 3(as indicated by the parenthetical 2, 3 in the sequence ordering column214 in rows having a sequence number of 1).

The asset modification process will transition to sequence 2 ifoperations E, F, and/or G become triggered and will transition tosequence 3 if operation H becomes triggered. If the asset modificationprocess transitions to sequence 2, operations E, F, and G are executable250, operations E, F, G, I, J, M, and N are triggerable 248, andoperations A-D, H, K, L, and O-Z are inactive 252. The assetmodification process remains in sequence state 2 until operations E, F,and G are detriggered in accordance with a detrigger indicator (whichmay result for a triggering of an operation in a next sequence state).Once detriggered, the asset modification process is again in thetransition state and may transition back into sequence state 2,transition to sequence state 4, or transition to sequence state 6 (e.g.,as indicated by the parenthetical 2, 4, 6 in the sequence orderingcolumn 214).

If the asset modification process transitions from sequence 1 tosequence 3, operation H is executable 250, operations H, J, K, L, and Iare triggerable 248, and operations A-G, M-Z are inactive 252. The assetmodification process remains in sequence state 3 until operation H isdetriggered in accordance with a detrigger indicator. Once detriggered,the asset modification process is again in the transition state and maytransition back into sequence state 3, transition to sequence state 4,or transition to sequence state 5 (e.g., as indicated by theparenthetical 2, 4, 5 in the sequence ordering column 214).

If the asset modification process transitions to sequence 4 from eithersequence 2 or 3, operations I and J are executable 250, operations C, D,K, and L are triggerable 248, and operations A, B, E-G, and O-Z areinactive 252. The asset modification process remains in sequence state 4until operations I and J are detriggered in accordance with a detriggerindicator. Once detriggered, the asset modification process is again inthe transition state and may transition to sequence state 1 ortransition to sequence state 5 (e.g., as indicated by the parenthetical1, 5 in the sequence ordering column 214).

If the asset modification process transitions to sequence 5 from eithersequence 3 or 4, operations K and L are executable 250, operations C, D,and Z are triggerable 248, and operations A, B, E-J, and M-Z areinactive 252. The asset modification process remains in sequence state 5until operations K and L are detriggered in accordance with a detriggerindicator. Once detriggered, the asset modification process is again inthe transition state and may transition to sequence state 1 ortransition to sequence state z (e.g., as indicated by the parenthetical1, z in the sequence ordering column 214).

If the asset modification process transitions to sequence 6 fromsequence 2, operations M and N are executable 250, operations K and Lare triggerable 248, and operations A-J and O-Z are inactive 252. Theasset modification process remains in sequence state 6 until operationsM and N are detriggered in accordance with a detrigger indicator. Oncedetriggered, the asset modification process is again in the transitionstate and may transition to sequence state 5 (e.g., as indicated by theparenthetical 5 in the sequence ordering column 214).

As an example, assume that the asset being modified is season tickets toa sporting event. Various operations for modifying season ticketsinclude, but are not limited to, opening season ticket assetmodification process, buy tickets for a particular sporting event, selltickets for a particular sporting event, keep the tickets for aparticular sporting event, determining a selling price, determining apurchasing price, determining a selling quantity, determining apurchasing quantity, closing the season ticket asset modificationprocess, etc.

For this example, sequence state 0 may correspond to opening the seasonticket asset modification process, which may be done by detecting anopening signal (e.g., operation A) or by detecting a trigger and executecondition for operation B (e.g., current date is 3 weeks prior tosporting event). When the season ticket asset modification process isopened, it is in a transition state waiting for a trigger condition tooccur, which may indicate to keep the tickets for the particularsporting event, buy more tickets to the sporting event, or sell ticketsto the sporting event. If a trigger condition occurs indicating keep thetickets (e.g., in town, good opponent, etc.), the asset modificationprocess for this particular sporting event is closed, but otherparticular sporting events may still be open.

If a trigger condition indicates buying more tickets (e.g., family orfriends in town), the asset modification process transitions to asequence for purchasing additional tickets. This sequence of purchasingadditional tickets may include several sequences (e.g., determiningwhether adjacent seats are available for purchase, determining anacceptable price, determining a quantity of extra tickets, purchasingthe extra tickets, etc.). The sequence of purchasing additional ticketsmay take a turn if adjacent seats are not available. In this instance, asequence for purchasing a group of best available tickets may be evokedand, if successful, then a sequence is evoked to sell the season ticketholder's tickets for the particular sporting event.

If a trigger condition indicates selling the tickets (e.g., out of town,event sold out, (opportunity to sell at greater than face value), etc.),the asset modification process transitions to a sequence, or series ofsequences to sell the tickets. The operations of the sequence or seriesof sequences include determining a number of tickets to sell, a sellingprice (which may change the closer to the event), fund distribution,etc.

FIG. 11 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset. In this example, the assetmodification module 30 selects one of the assets 254 to manage andretrieves the operational data 264 regarding the asset in accordancewith a selection process (e.g., user selection, automated determinationprocess, default selection, etc.). The operation data 264 of an assetincludes an asset ID, the asset, and/or information regarding the asset(e.g., quantity, value, use rate, etc.).

Once the asset modification module 30 selects an asset 254 formodification, it selects an operation set limit table 266 from aplurality of limit tables 256. In this example, asset 1 has limit tables1_(—)1 through 1_α available and the asset modification module 30selects one of them based on attributes and/or factors of the limittables (e.g., risk levels, reliability, etc.) in accordance with userpreferences or a calculated preference.

The asset modification module 30 accesses the selected limit table toretrieve one or more rows of information 268 (e.g., a row of informationcorresponds to information regarding an operation, which may be in aparticular sequence order). The asset modification module 30 interpretsthe information 270 to identify one or more evaluation data 272 andretrieves the corresponding evaluation data 280. The asset modificationmodule 30 further interprets the information to identify indicators 274(e.g., trigger, detrigger, activate, deactivate, etc.). The assetmodification module 30 also interprets the information to identify, andretrieve, one or more operations 278 from a pool of operations 258.

Having retrieved indicators and the operation(s), the asset modificationmodule 30 analyzes the retrieved evaluation data 260, which is data thatis time varying and time sensitive and may further be streaming data, inlight of the indicators. When a trigger indicator is met, the operation258 is triggered and the status of the operation within the limit table256 is updated accordingly 276. When an activate indicator is met, theoperation 258 is activated for execution and the status of the operationwithin the limit table 256 is updated accordingly 276. The assetmodification module 30 executes an activated operation 282 in accordancewith the indicators of the limit table 256 to produce a partialmodification resultant.

The asset modification module 30 continues to access the limit table266, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify operations 284, and execute the operations 282in accordance with the identifiers. Once the modification of the assetis closed, the asset modification module 30 outputs an assetmodification result 262.

FIG. 12 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset 254 (e.g. creating the assetor adding to the asset 286). In this example, the asset modificationmodule 30 selects one of the assets 254 to create, or add to, and usesthis information to select an operation set limit table 266 from aplurality of limit tables 256.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information 270 (e.g., a row ofinformation corresponds to information regarding an operation, which maybe in a particular sequence order). The asset modification module 30interprets the information to identify one or more evaluation data 272and retrieves the corresponding evaluation data 280. The assetmodification module 30 further interprets the information to identifyindicators 274 (e.g., trigger, detrigger, activate, deactivate, etc.).The asset modification module 30 also interprets the information toidentify 284, and retrieve 278, one or more operations from a pool ofoperations 258.

Having retrieved indicators and the operation(s) 258, the assetmodification module 30 analyzes the retrieved evaluation data 260. Whena trigger indicator is met, the operation 258 is triggered and thestatus of the operation within the limit table 256 is updatedaccordingly 276. When an activate indicator is met, the operation 258 isactivated to create and/or add to the asset 254 and the status of theoperation within the limit table 256 is updated accordingly 276.

The asset modification module 30 continues to access the limit table256, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify operations 284, and execute the operations 282in accordance with the identifiers to create and/or add to the asset.Once the modification of the asset is closed, the asset modificationmodule 30 outputs an asset modification result 262 (e.g., the generatedor updated asset).

FIG. 13 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset. In this example, the assetmodification module 30 selects one of the assets 254 to manage andretrieves the operational data 264 regarding the asset in accordancewith a selection process. The asset modification module 30 also selects290 an operation set 288 with included limit table from a plurality ofoperation sets with included limit tables. In this example, an operationset 288 has its own limit table, as such the operation set (e.g., set ofoperations that may be performed) is paired with a limit table.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information 270 (e.g., a row ofinformation corresponds to information regarding an operation, which maybe in a particular sequence order). The asset modification module 30interprets the information to identify one or more evaluation data 272and retrieves 280 the corresponding evaluation data 260. The assetmodification module 30 further interprets the information to identifyindicators 274 (e.g., trigger, detrigger, activate, deactivate, etc.).The asset modification module 30 also interprets the information toidentify one or more operations from the operation set 288.

Having retrieved indicators and the operation(s), the asset modificationmodule 30 analyzes the retrieved evaluation data 260 in light of theindicators. When a trigger indicator is met, the operation is triggeredand the status of the operation within the limit table is updatedaccordingly 276. When an activate indicator is met, the operation isactivated for execution and the status of the operation within the limittable is updated accordingly 276. The asset modification module 30executes an activated operation 282 in accordance with the indicators ofthe limit table to produce a partial modification resultant.

The asset modification module 30 continues to access the limit table,identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify operations, and execute the operations 282 inaccordance with the identifiers. Once the modification of the asset isclosed, the asset modification module 30 outputs an asset modificationresult 262.

FIG. 14 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset (e.g. creating the asset oradding to the asset 286). In this example, the asset modification module30 selects one of the assets 254 to create or add to and uses thisinformation to select an operation set and corresponding limit table 290from a plurality of operation sets and corresponding limit tables 288.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information 270 (e.g., a row ofinformation corresponds to information regarding an operation, which maybe in a particular sequence order). The asset modification moduleinterprets the information to identify one or more evaluation data 272and retrieves the corresponding evaluation data 280. The assetmodification module 30 further interprets the information to identifyindicators 274 (e.g., trigger, detrigger, activate, deactivate, etc.).The asset modification module 30 also interprets the information toidentify one or more operations from the set of operations 288.

Having retrieved indicators and the operation(s), the asset modificationmodule 30 analyzes the retrieved evaluation data. When a triggerindicator is met, the operation is triggered and the status of theoperation within the limit table is updated accordingly 276. When anactivate indicator is met, the operation is activated to create and/oradd to the asset and the status of the operation within the limit tableis updated accordingly 276.

The asset modification module 30 continues to access the limit table268, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify operations, and execute the operations 282 inaccordance with the identifiers to create and/or add to the asset. Oncethe modification of the asset is closed, the asset modification module30 outputs an asset modification result 282 (e.g., the generated orupdated asset).

FIG. 15 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset. In this example, the assetmodification module 30 selects one of the assets 254 to manage andretrieves the operational data 264 regarding the asset in accordancewith a selection process. The asset modification module 30 also selectsan operation set from a pool or operation sets 294 and selects a limittable 266 from a pool of limit tables 256.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information 270 (e.g., a row ofinformation corresponds to information regarding an operation, which maybe in a particular sequence order). The asset modification moduleinterprets the information to identify one or more evaluation data 272and retrieves the corresponding evaluation data 280. The assetmodification module 30 further interprets the information to identifyindicators 274 (e.g., trigger, detrigger, activate, deactivate, etc.).The asset modification module 30 also interprets the information toidentify one or more operations from the selected operation set 294.

Having retrieved indicators and the operation(s), the asset modificationmodule 30 analyzes the retrieved evaluation data 260 in light of theindicators. When a trigger indicator is met, the operation is triggeredand the status of the operation within the limit table is updatedaccordingly 276. When an activate indicator is met, the operation isactivated for execution and the status of the operation within the limittable is updated accordingly 276. The asset modification module 30executes an activated operation 282 in accordance with the indicators ofthe limit table to produce a partial modification resultant.

The asset modification module 30 continues to access the limit table268, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify operations, and execute the operations 282 inaccordance with the identifiers. Once the modification of the asset isclosed, the asset modification module 30 outputs an asset modificationresult 262.

FIG. 16 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset (e.g. creating the asset oradding to the asset 286). In this example, the asset modification module30 selects one of the assets 254 to create or add to and uses thisinformation to select an operation set from a pool of operation sets 294and to select a limit table 266 from a pool of limit tables 256.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information 270 (e.g., a row ofinformation corresponds to information regarding an operation, which maybe in a particular sequence order). The asset modification module 30interprets the information to identify one or more evaluation data 272and retrieves the corresponding evaluation data 280. The assetmodification module 30 further interprets the information to identifyindicators 274 (e.g., trigger, detrigger, activate, deactivate, etc.).The asset modification module 30 also interprets the information toidentify one or more operations from the set of operations 294.

Having retrieved indicators and the operation(s), the asset modificationmodule 30 analyzes the retrieved evaluation data 260. When a triggerindicator is met, the operation is triggered and the status of theoperation within the limit table is updated accordingly 276. When anactivate indicator is met, the operation is activated to create and/oradd to the asset and the status of the operation within the limit tableis updated accordingly 276.

The asset modification module 30 continues to access the limit table266, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify operations 270, and execute the operations 282in accordance with the identifiers to create and/or add to the asset.Once the modification of the asset is closed, the asset modificationmodule 30 outputs an asset modification result 262 (e.g., the generatedor updated asset).

FIG. 17 is a schematic block diagram of another embodiment of an assetmodification module 30 that includes a limit table interface module 296,a plurality of indicator buffers 298-304, memory 306-308, an operationselection module 310, a specific task module (i.e. an operationexecution module) 312, a resultant analysis module 314, a triggeroperation execution module 316, an execute/pause (activate/deactivate)operation execution module 318, and an evaluation data filter 320. Theindicator buffers 298-304 include an operation data indicator buffer298, an evaluation data indicator buffer 300, a trigger indicator buffer302, and an execute/pause (activate/deactivate) indicator buffer 304.The memory 306-308 stores filtered evaluation data 306, operation data308, and may further store the resultant(s) of the operation executionmodule 312. Note that each of the modules may be implemented as asoftware module executable by a processing module, may be implemented asa hard coded module, and/or may be implemented as a firmware module(e.g., a combination of software and hardware).

In an example of operation, the limit table interface module 296interfaces with a selected limit table 322 to retrieve one or more rowsof information, where the per row information includes sequence(ordering), operation identifier (ID), one or more trigger indicators(TI), one or more detrigger indicators (DT), evaluation data indicators(EDI), execute (or activate) indicators (EI), pause (or deactivate)indicators (PI), operational data indicators (ODI), current operationstatus, and/or execution module selection. The one or more rows ofinformation will correspond to the present and/or next sequences of theasset modification process. For example, when commencing the assetmodification process, the limit table interface module 296 will retrievethe one or more rows of information with a sequence number of 0. Asanother example, when the asset modification process is in a sequencetransition state, the limit table interface module 296 will retrieve theone or more rows of information for the next possible sequence, orsequences. Alternatively, the limit table interface module 296 mayretrieve row information from the limit table 322 from one row at a timeto all rows of the limit table 322.

The limit table interface module 296 provides the retrieved indicatorsto the respective indicator buffers 298-304 for temporary storagetherein. For example, the limit table interface module 296 provides thetrigger indicator(s) and the detrigger indicator(s) to the triggerindicator buffer 302; provides the execute (activate) indicator(s) andthe pause (deactivate) indicator(s) to the execute/pause indicatorbuffer 304; provides the evaluation data indicator(s) to the evaluationdata buffer 300; and provides the operation data indicator(s) to theoperation data indicator buffer 298. The limit table interface module296 provides the operation ID of the operation data indicator(s) to theoperation selection module 310.

The operation selection module 310 retrieves one or more operations 324based on the operation ID(s) and temporarily stores them. The operationselection module 310 may retrieve the operation(s) 324 from a pool ofoperations, from the limit table that includes in the operations, and/orfrom an operation set identified by the limit table. Further, theoperation selection module 310 may retrieve the operation 324 as analgorithm for execution by the operation execution module 312. Forexample, the operation 324 may be a logic function, a mathematicalfunction, an algorithm, and/or an operational instruction. As morespecific examples but far from an exhaustive list, the operation 324 isan algorithm to purchase stock; is an algorithm to sell sporting eventtickets; is an algorithm to update a file on a person with newly foundevaluation data; is an algorithm to determine when to place a purchaseorder for a component of a manufactured product; is an algorithm togenerate an alarm when the national weather service has issued a severestorm warning for a region; and/or is a function to change theformatting of a selected evaluation data.

The evaluation data filter 320 retrieves evaluation data 326 based onthe evaluation data indicator(s) and filters the data in accordance withthe evaluation data indicator(s). The evaluation data filter 320, whichfunctions as previously described with reference to FIGS. 7-8A, providesfiltered evaluation data to the filtered evaluation data memory 306 forstorage therein.

The trigger operation execution module 316 functions like the wake-upsleep module of FIGS. 7 and 8 to interpret the filtered evaluation datain light of the trigger indicator(s) to determine whether to trigger anoperation. When the operation is to be triggered, the trigger operationexecution module 316 provides a trigger indicator to the operationselection module 310. The operation selection module 310 records thetriggering of the operation and may load the operation into theoperation execution module 312 depending on current use of the operationexecution module 312. For instance, if this is the only operationtriggered and no other operations are activated, then the operationselection 310 may load the operation 328 into the execution module 312.If, however, the execution module 312 is executing an operation, theoperation selection module 310 will wait to load the specific task (i.e.operation) 328 into the execution module 312.

The execute/pause operation execution module 318 functions like theexecute activate/deactivate module of FIGS. 7 and 8 to interpret thefiltered evaluation data in light the activate (execute) indicator(s) todetermine whether to activate an operation. When the operation is to beactivated, the execute/pause operation execution module 318 provides anactivation indicator to the operation selection module 310, which itrecords. The operation selection module 310 controls program execution,interrupts, and multitasking for the operation execution module, suchthat the operation execution module 310 may execute one or moreoperations efficiently. As such, when an operation is activated, theoperation selection module 310 enables the operation execution module312 to execute the operation. Alternatively, the asset modificationmodule 30 may include a plurality of operation execution modules, wherethe operation selection module 310 selects with operation executionmodule to execute which activated operation.

The operation execution module 312 executes a selected operation 328 onoperation data 330, filtered evaluation data, and/or a previousresultant to produce a resultant 332. The resultant 332 corresponds to amodification of the asset. For example, purchase stock, sell stock, sellsporting event tickets, buy sporting event tickets, update a person'sfile, issue a severe weather warning, etc.

The resultant analysis module 314 analyzes the resultant 332 todetermine whether to deactivate and/or detrigger the present operation,to place the asset modification process in a transition state, totransition the asset modification process to a next sequence state, etc.In addition, the resultant analysis module 314 provides statusinformation to the limit table interface module 296 such that it canupdate the status field within the limit table 322 regarding the presentoperation.

For multitasking of several rows of information of a limit table 322, auser device or service provider device may include multiple assetmodification modules 30, each processing one or more assigned rows ofinformation. Additionally, or in the alternative, the asset modificationmodule 30 manages multitasking of the operations at each level of themodule (e.g., the evaluation data filter 320, operation execution module312, the execute/pause operation execution module 318, the triggeroperation execution module 316, etc.). As another alternative, the assetmodification module 30 includes a plurality of evaluation data filters,a plurality of operation execution modules, a plurality of execute/pauseoperation execution modules, and/or a plurality of trigger operationexecution modules.

FIG. 17A is a schematic block diagram of another embodiment of an assetmodification module 30 operably coupled to an operational processingmodule (e.g., operation module) 334. The asset modification module 30includes a limit table interface module 296, a plurality of indicatorbuffers 298-304, memory 306-308, an operation selection module 310, anda resultant analysis module 314. The indicator buffers 298-304 includean operation data indicator buffer 298, an evaluation data indicatorbuffer 300, a trigger indicator buffer 302, and an execute/pause(activate/deactivate) indicator buffer 304. The memory 306-308 storesfiltered evaluation data 306, operation data 308, and may further storethe resultant(s) of the operation execution module 312. Note that eachof the modules may be implemented as a software module executable by aprocessing module, may be implemented as a hard coded module, and/or maybe implemented as a firmware module (e.g., a combination of software andhardware). The operation module 334 includes a specific task executionmodule (e.g., an operation execution module) 312, trigger operationexecution module 316 and an execute/pause (activate/deactivate)operation execution module 318), and an evaluation data filter 320. Notethat the operation module 334 may further include a filtered dataanalysis module, which may be a separate module or embedded in one ormore of the evaluation data filter 320, the execute/pause module 318,and the trigger module 316.

In an example of operation, the asset modification module 30 identifiesa selected operation module 334 and interfaces with it. The interfacingmay be locally (e.g., via a computing device bus structure, anapplication program interface (API), etc.) or may be remotely (e.g., viaa WLAN interface, a LAN interface, a WAN interface, an Internetinterface, etc.). Once the asset modification module 30 is operablycoupled to the selected operation module 334, the coupled pair functionsin a similar manner as the asset modification module 30 of FIG. 17. Notethat the asset modification module 30 may be operably coupled tomultiple selected operation modules at a given time using a multitaskingfunction.

FIGS. 18-20 are a logic diagram of another method for modifying an assetin accordance that may be performed by the asset modification module ofFIGS. 17 and/or 17A. The method begins with the asset modificationmodule selecting a limit table for a particular asset 336. The methodcontinues with the asset modification module determining an initialsequence based on one or more entries of the limit table 338. Forexample, the asset modification module identifies the operations havinga sequence number of 0 and may further identify the operations havingthe next one or more sequence numbers. As a specific example, the assetmodification module may identify the sequence number and ordering frominformation contained in the limit table or it may determine it based onthe operations, evaluation data being analyzed, and/or the indicators.

The method then branches into three branches. In the first branch, theasset modification module identifies evaluation data indicators for theoperation(s) of the initial sequence (e.g., sequence 0) from the limittable 340. This branch continues with the asset modification moduledetermining whether there is evaluation data to retrieve 342. If yes,the branch continues with the asset modification module retrieving theidentified evaluation data 344. If not, or after the evaluation data isretrieved, this branch continues on FIG. 19.

In the second branch, the asset modification module identifies one ormore operations associated with the initial sequence 346. For exampleand with reference to FIGS. 10A-10C, the asset modification moduleidentifies operations A and B. This branch continues with the assetmodification module retrieving the one or more operations 348. Forexample, the asset modification module of FIG. 17 may retrieve operationinstructions of an algorithm corresponding to the operation(s) of theinitial sequence. As another example, the asset modification module ofFIG. 17A establishes an operable coupling with the operation modules ofthe initial sequence. This branch continues with the asset modificationmodule retrieving the indicators (trigger, detrigger, activate, anddeactivate) for each of the operations 350. This branch then continueson FIG. 19.

In the third branch, the asset modification module identifies operationdata indicators for the selected operations of the initial sequence 352.This branch continues with the asset modification module determinewhether there is at least one operation data indictor (e.g., operationdata ID, data allotment, and/or data operands) to retrieve 354. Forexample, if the operations for the initial sequence are to open an assetmodification process of the limit table, there may not be any operationdata indicators to retrieve. If there is an operation data indicator toretrieve, this branch continues with the asset modification moduleretrieving it 356. Once the asset modification module has, or if thereare no operation data indicators to retrieve, this branch continues onFIG. 19.

On FIG. 19, the second branch continues with the asset modificationmodule determining whether the operation set is complete 358. Forexample, the asset modification module is determining whether the assetmodification process for the selected asset is being closed. If yes, themethod continues with the asset modification module updating 360, ifneeded, status fields in the limit table indicating that the assetmodification process is being closed 362. If the asset modificationprocess is not being closed, the method continues with the assetmodification module and/or the selected operation module determiningwhether a trigger indicator for an operation is met 364. If not, themethod remains in a loop until the asset modification process is closedor an operation is triggered. Note that the first branch of FIG. 18 tiesinto the step of determining whether a trigger indicator is met.

When a trigger indicator is met, the method continues with the assetmodification module and/or selected operation module triggering theoperation 366. The method continues with the asset modification moduleand/or selected operation module determining whether an execution (oractivate) indicator is met 368. If not, the method branches back to thestep of determining whether a trigger indicator is met for anotheroperation 364. Also, for the current triggered operation, the methodcontinues with the asset modification module and/or selected operationdetermining whether a detriggering indicator is met 370. If yes, thecurrent operation is detriggered 372. If not, the method loops back todetermining whether an activate indicator is met 368.

When, for a triggered operation, an activate indicator is met, themethod continues with the asset modification module and/or selectedoperation executing the operation to produce a result 374. The methodcontinues with the asset modification module (via the results analysismodule) determining whether the results indicate whether the assetmodification process should proceed to the next sequence 376. If yes,the method continues with the asset modification module and/or selectedoperation module stopping execution of the operations of the currentsequence state and placing them in a triggered state or a detriggeredstate depending on the next sequence 378. For example, if the nextsequence could be a repeat of the current sequence, then the operationsare placed in the triggered state. If, however, the next sequence couldnot be a repeat of the current sequence, then the operations are placedin a detriggered state. Regardless of the state of the operations areplaced in, the method continues on FIG. 20.

If the results of the executed operation do not indicate transitioningto the next sequence state, the method continues with the assetmodification module and/or the selected operation module determiningwhether a deactivate (or pause) indicator is met for the operation 380.If not, the method continues in three branch back paths. The firstbranch back path is for the operation being executed, which loops backto the step of executing the operation 374. The second branch back pathis for triggered operations, which loops back to the step of determiningwhether an activation indicator is met 368. The third branch back pathis for other operations of the current sequence that are not yettriggered, which loops back to the step of determining whether a triggerindicator is met 358.

If a deactivate indicator is met, the method continues with the assetmodification module and/or the selected operation module stoppingexecution of the operation 382. The method then continues with the assetmodification module determining whether other operations are stillexecuting (e.g., are still activated) 384. If not, the method continuesin two branch back paths. The first branch back path is for triggeredoperations, which loops back to the step of determining whether anactivation indicator is met 368. The second branch back path is forother operations of the current sequence that are not yet triggered,which loops back to the step of determining whether a trigger indicatoris met 358.

When another operation is still executing, the method continues in threebranch back paths. The first branch back path is for the operation beingexecuted, which loops back to the step of executing the operation 374.The second branch back path is for triggered operations, which loopsback to the step of determining whether an activation indicator is met368. The third branch back path is for other operations of the currentsequence that are not yet triggered, which loops back to the step ofdetermining whether a trigger indicator is met 358.

When the asset modification process is in a transition state, the methodcontinues on FIG. 20 with the asset modification module determiningwhether end the operation set (e.g., close the asset modificationprocess) 386. If yes, the method continues with the asset modificationmodule updating status 388 in the limit table and outputting aresultant, if any 390.

If the asset modification process is not being closed, the methodcontinues with the asset modification module transitioning the processto the next sequence, which branches into three branches. In the firstbranch, the asset modification module identifies evaluation dataindicators for the operation(s) of the next sequence, or sequences, fromthe limit table 392. This branch continues with the asset modificationmodule determining whether there is evaluation data to retrieve 394. Ifyes, the branch continues with the asset modification module retrievingthe identified evaluation data 396. If not, or after the evaluation datais retrieved, this branch continues on FIG. 19.

In the second branch, the asset modification module identifies one ormore operations associated with the next sequence(s) 398. This branchcontinues with the asset modification module retrieving the one or moreoperations 400. For example, the asset modification module of FIG. 17may retrieve operation instructions of an algorithm corresponding to theoperation(s) of the initial sequence. As another example, the assetmodification module of FIG. 17A establishes an operable coupling withthe operation modules of the initial sequence. This branch continueswith the asset modification module retrieving the indicators (trigger,detrigger, activate, and deactivate) for each of the operations 402.This branch then continues on FIG. 19.

In the third branch, the asset modification module identifies operationdata indicators for the selected operations of the next sequence(s) 404.This branch continues with the asset modification module determinewhether there is at least one operation data indictor (e.g., operationdata ID, data allotment, and/or data operands) to retrieve 406. If thereis an operation data indicator to retrieve, this branch continues withthe asset modification module retrieving it 408. Once the assetmodification module has, or if there are no operation data indicators toretrieve, this branch continues on FIG. 19.

FIG. 21 is a schematic block diagram of an example of an assetmodification module 30 selecting a limit table for asset modification.The asset modification module 30 includes an asset management function410 (e.g., software and/or hardware module), which functions to selectan asset to modify and a limit table to provide the asset modificationprocess.

In an example of selecting an asset, the asset management function 410receives asset selection criteria 412, which includes, but is notlimited to, an asset ID, time of day, a time period, evaluation dataanalysis for a particular result, etc. The asset selection criteria 412may be a user input and/or may be automatically generated. For example,asset selection criteria 412 may include an asset ID, a start time ofday, an end time of day, and day of week. As a more specific example,the asset selection criteria 412 may include an asset ID for aparticular stock for which an asset modification process is to beengaged from 9 AM to 4 PM eastern time on Monday through Friday. Asanother example, the asset selection criteria 412 may include analysisof an inventory list for a product, where, when an inventory level fallsbelow a certain threshold for a particular component, identify theparticular component as the asset for an asset modification process. Asyet another example, asset selection criteria 412 may includeavailability of certain evaluation data and may further include anevaluation data analysis criterion.

The asset management function 410 compares the asset selection criteria412 with factors for assets 414-416 of a pool of assets to determinewhether an asset modification process should be engaged for a particularasset. The factors 414-416 include, but are not limited to, a type ofasset, modification timing (e.g., time of day, inventory depletion,etc.), evaluation data sources of interest availability, evaluation dataanalysis result (e.g., pattern mapping, trend detection, valuethresholds, comparative analysis, etc.). As an example, asset selectioncriteria 412 may include availability of a particular evaluation datasource(s) and an asset factor includes, when the particular evaluationdata is available, engage an asset modification process for the asset.As another example, asset selection criteria 412 include an evaluationdata analysis that produces a result that correlates with an evaluationdata analysis result of the factors of an asset 414-416.

Once an asset is selected for modification, the asset managementfunction 410 selects a limit table based on limit table selectioncriteria 418 and attributes of limit tables 420 associated with theselected asset. The limit table selection criteria 418 include userinputs and/or auto-generated inputs regarding, but not limited to,desired risk level, desired level of evaluation data relevancy, assetmodification philosophy, desired reliability level, desired level ofevaluation data mapping (to trends, patterns, values, transitions,slopes, quantities, pricing, availability, etc.), a desired level ofperformance, etc. The attributes of a limit table 420 include one ormore of, but not limited to, risk level, evaluation data relevancy,asset modification philosophy, reliability level, evaluation datamapping accuracy, performance information, etc.

The risk level corresponds to a risk-reward relationship of modifyingthe asset. A low risk level indicates a relatively low risk that theasset will be modified in a modest favorable manner. As an example forinventory control of a manufacturing process, the risk-rewardrelationship will vary based on whether price, on-hand availability,shipping, suppliers, etc. is/are a primary priority. As such, the risklevel corresponds to how willing a user is to compromise adverselyaffecting the manufacture of a product to get a desired reward for aparticular aspect of a component, or components.

The relevancy of evaluation data corresponds to how relevant theevaluation data has been in the past as a source for a predictablemodification of an asset. The more predictable the modification of assetis based on analysis of a particular evaluation data, the more relevantthe particular evaluation data. The relevancy of evaluation data mayalso include a weighting factor based on how many times the evaluationdata has been accessed for modifying an asset and/or, of the timesaccessed, how many times has it been used to provide a predictablemodification of the asset.

The reliability level corresponds to how reliable the limit table hasbeen in producing a favorable asset modification. The more consistentlythe limit table produces similar asset modification results, the morereliable it is. Conversely, the more varied the asset modificationresults, the lower the reliability is of the limit table.

The evaluation data mapping corresponds to how close aspects (e.g.,trends, patterns, values, waveform, transitions, slopes, quantities,pricing, availability, etc.) of evaluation data are to be mapped to tripa trigger indicator and/or an activation indicator. For example, if anevaluation data is being analyzed for a specific waveform, theattributed indicates how closely the waveform of the evaluation dataneeds to match the specific waveform to trip an indicator.

The performance level corresponds to previous performances of the limittable. The performance level may include information regarding amount ofprevious asset modifications, variations of previous assetmodifications, average of previous asset modifications, etc.

Accordingly, for a selected asset, the asset management function 410selects a limit table having attributes 420 that correlate to the limittable selection criteria 418. When a near exact match cannot be made(e.g., with acceptable tolerances of a few percent to tens of percent),the asset modification function 410 selects a limit table having a mostfavorable correlation of its attributes 420 to the limit table selectioncriteria 418. Further, the asset modification function 410 may have athreshold for determining a most favorable correlation of a limittable's attributes to the limit table selection criteria 418. If thethreshold is not met, then the asset modification module 410 does notselect a limit table. If this occurs, the asset modification module 410may trigger generating a limit table that has attributes 420 that morefavorably correlate to the limit table selection criteria 418.

FIG. 22 is a diagram of an example of an asset's factors 422, which aretime varying and updated on a regular basis (e.g., by the minute,hourly, daily, etc.) based on evaluation data. In this example, theasset includes a plurality of factors 422 (A through X). A factor 422may be determined based on performance information of various limittables over time, where each factor corresponds to a particularevaluation data set A-X (which includes one or more evaluation data).For a given evaluation data set, its affects on each limit table for agiven asset is analyzed to produce limit table performance information.As is shown in this example, limit tables “a” through “k” for asset 1are being processed. Note that similar processing may be done for one ormore other limit tables of one or more other assets.

As is also shown, time 424 goes from left to right of the illustration(e.g., older data to the right and newer data to the left). The time isdivided into frames, where the evaluation data is analyzed during aparticular time frame. The duration of a time frame may vary frommicroseconds, seconds, minutes, hours, days, weeks, and/or years. Thecontribution to a factor may be weighted by giving more or less weightto more recent data. Other weighting factors may include time of day,quantity of data, source, deviations from past patterns, etc.

For factor A, the more recent set of evaluation data is analyzed foreach of the limit tables a-k for asset 1 to produce performanceinformation. The performance information includes, for a limit table,data values, patterns, volatility, slopes, deviations, pattern repeatingprobabilities, pattern transition tendencies, trend probabilities, trendtransition tendencies, etc. and how the modification of the asset isaffected. For example, certain patterns may cause the asset to beconsistently modified in a particular way, while other patterns have nopredictable correlation to how the asset is modified. As anotherexample, various combinations of the performance information providespredictable modifications of an asset at a given predictability rate(e.g., 75% of the time). Such information is compiled and interpreted togenerate a corresponding factor, which may include a favorable pattern,an unfavorable pattern, a desire data value, a desired slope, acceptabledata volatility, etc. Further, the various factors of an asset may bedetermined based on the performance information. For example,modification timing may be determined based on the performanceinformation.

The other factors are processed in a similar way to produce limit tableperformance information for each of the limit tables a-k for asset 1.The factors can be used to select with evaluation data sets to use,which limit table to use, etc. Note that the factors are updated byadding more recent time of data sets, by deleting older times of datasets, changing evaluation data within a set of evaluation data, changinga limit table, adding a limit table, deleting a limit table, changinguser performance preferences, etc.

FIG. 23 is a diagram of an example of attributes of limit tables, whichare time 424 varying and updated on a regular basis (e.g., by theminute, hourly, daily, etc.) based on evaluation data 426-430. In thisexample, the attributes for a limit table include a risk level,evaluation data relevancy, asset modification philosophy, reliabilitylevel, pattern mapping, etc., which, for each limit table, is determinedbased on the performance information of the limit table collected overtime.

In addition, one or more experimental limit tables may be tested overtime to determine its attributes. Depending on the quality of theattributes, the limit table may be added to a list of usable limittables. If the quality of the attributes is not at a desired level, oneor more entries of the limit may be changed and further testing may bedone to obtain a higher quality of the attributes.

FIG. 24 is a logic diagram of a method of opening an asset as may beperformed by an asset management function. The method begins with theasset management function monitoring asset selection criteria, which maybe one or more of a user input, analysis of evaluation data, time ofday, an evaluation data source, availability of new evaluation data,etc. 432. The method continues with the asset management functiondetermining whether the asset selection is auto enabled or signalenabled 434.

When the asset selection is signal enabled (e.g., a user provides aninput to select a particular asset), the method continues with the assetmanagement function determining whether the select signal is detected436. Once the signal is detected, the method continues with the assetmanagement function opening the asset and placing it on an open list(e.g., a list of assets that are currently open) 438. Alternatively, astatus flag associated with the asset may be set to an open status. Themethod then proceeds to the selecting a limit table for the asset 440,which is discussed in FIG. 25.

When the asset selection is auto enabled, the method continues with theasset management function analyzing factors of each evaluation data setwith respect to current evaluation data 442. Recall that factors includeone or more of asset type, modification timing, evaluation data sourcesof interest, evaluation data pattern mapping, which includes favorablepatterns, unfavorable patterns, desired data values, desired dataslopes, acceptable data volatility, favorable comparison betweenevaluation data, and/or other favorable analysis of one or moreevaluation data. The current evaluation data may include one or more ofthe data sets associated with an asset portfolio. As an example, themethod may be configured to determine if a particular asset should beauto opened. In this instance, the evaluation data set(s) would belimited to the sets associated with the particular asset. As anotherexample, the method may be configured to determine if any one or more ofthe assets should be opened. In this instance, the evaluation data setswould not be limited to a particular asset.

The analysis of the factors may include a mathematical operation, alogic operation, a filtering operation, a statistical operation, acombination thereof, multiple combinations thereof, and/or multipleiterations of one or more of the operations. For instance, the analysismay include an averaging function, a standard deviation function, anormalizing function, a root mean square function, weighting function,digital logic function (e.g., OR, AND, NOR, XOR, etc.), etc.

The method continues with the asset management function selecting anevaluation data set based on the analysis 444. For example, currentevaluation data set A is tracking within desired tolerances of thefactors for a given asset. If more than one evaluation data set istracking within desired predictable aspects, one or more of them mightbe selected. For example, the asset management function may select theone that is more closely maps to desired patterns, trends, etc. Asanother example, the asset management function may select severalevaluation data sets.

The method continues with the asset management function monitoring thecurrent evaluation data of the selected evaluation data set(s) in lightof the factors of an associated asset 446. For example, the currentevaluation data is monitored for patterns, trends, etc. indicating that,in the near future (in the next few milliseconds, the next seconds, thenext minutes, the next hours, the next days, the next weeks, the nextmonths, etc.), that favorable asset modification is probable and mayfurther indicate an indication as to a level of probability. Forexample, when, in the past, the evaluation data exhibited certainpatterns, trends, etc. favorable asset modification occurred at acertain ratio (e.g., 10 out of 10, 6 out of 11, etc.).

The method continues with the asset management function determiningwhether the factors compare favorably to the relevant asset selectioncriteria (e.g., a set of criterion expressing a desired level ofprobability for each factor of interest, which may be weighted) 448. Forinstance, trends or patterns may be given more weight than theevaluation data being at a specific data value. When the comparison isfavorable, the method continues with the asset management functionopening the asset and placing it on an open list (or setting a statusflag) 438. The method then proceeds to the selecting a limit table forthe asset 440, which is discussed in FIG. 25.

If the comparison was not favorable, the method continues by determiningwhether there is another asset not open 450. If not, the method waitsuntil one or more assets are not open. If there is at least one assetnot open, the method goes to the next asset 452 and repeats the methodat the analysis factors step 442.

FIG. 25 is a logic diagram of a method of selecting a limit table for anasset as may be performed by an asset management function. The methodbegins with the asset management function determining whether the limittable selection is auto enabled or signal enabled 454. When the limitselection is signal enabled (e.g., a user provides an input to select aparticular asset), the method continues with the asset managementfunction determining whether the select signal is detected 456. Once thesignal is detected, the method continues with the asset managementfunction opening the limit table 458. The method continues with theasset management function determining whether to open another limittable for the asset 460. If not, the method is complete for this assetmodification process.

If the limit table selection is automated, the method continues with theasset management function analyzing attributes of one or more limittables with respect to an evaluation data set (e.g., the selectedevaluation data set) 462. Recall that the attributes include, but arenot limited to, one or more of risk level, evaluation data relevancy,asset modification philosophy, reliability level (e.g., proven,unproven, works some times, etc.), favorable evaluation data patternsand/or trends, performance information, etc. The limit table toinitially analyze may be randomly chosen, may be based on history of usefor the asset, based on indicators, based on the selected evaluationdata set, etc.

The method continues with the asset management function analyzingattributes of the one or more limit tables with respect to userperformance preferences, which include, but are not limited to, adesired risk level, a specific asset modification philosophy, a desiredreliability level, a desired level of evaluation data mapping, a desiredperformance level, etc. 464. Such a determination may be done by a tablelook up, a query-response process, receiving user inputs, etc.

The method continues with the asset management function determiningwhether the analysis was favorable 466. If yes, the method continueswith the asset management function opening the limit table 458. Themethod continues with the asset management function determining whetherto open another limit table for the asset 460. If not, the method iscomplete for this asset modification process.

If the attributes did not compare favorably, the method continues withthe asset management function determining whether all limit tables havebeen tested 468. If not, another limit table is selected and the processrepeats as shown 470. If all of the limit tables have been tested, themethod continues with the asset management function determining whetherto select another evaluation data set and/or to change the userpreferences 472. If not, the process is closed and a limit table is notopened 474. If yes, the method repeats as shown.

FIG. 26 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset. In this example, the assetmodification module 30 selects one of the assets 254 to manage andretrieves the operational data regarding the asset in accordance with aselection process 264. Once the asset modification module 30 selects anasset for modification, it selects an operation set limit table 266 froma plurality of limit tables 256. In this example, asset 1 has limittables 1_(—)1 through 1_α available and the asset modification module 30selects one of them based on attributes and/or factors of the limittables (e.g., risk levels, reliability, etc.) in accordance with userpreferences or a calculated preference.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information 270 (e.g., a row ofinformation corresponds to information regarding an operation, which maybe in a particular sequence order). The asset modification module 30interprets the information to identify one or more evaluation data 272and retrieves the corresponding evaluation data 280. The assetmodification module 30 further interprets the information to identifyindicators 274 (e.g., trigger, detrigger, activate, deactivate, etc.).The asset modification module 30 also interprets the information toidentify one or more co-processors from a pool of co-processors 476 toperform a particular operation (e.g., operation 1, k, x, Ψ).

Having retrieved indicators and identified the co-processor(s), theasset modification module 30 provides the retrieved evaluation data 260and the indicators to the identified co-processor(s) for analysis 478.When a trigger indicator is met, the co-processor triggers an operationand informs the asset modification module 30 thereof. The assetmodification module 30 updates the status of the operation within thelimit table 276. When an activate indicator is met, the co-processoractivates the operation for execution and informs the asset modificationmodule 30 thereof. The asset modification module updates the status ofthe operation within the limit table 276. The co-processor executes anactivated operation in accordance with the indicators of the limit tableto produce a partial modification resultant 480, which it provides tothe asset modification module 30. Alternatively, the asset modificationmodule 30 analyzes the evaluation data 482 to determine the triggeringand activation of an operation. In this alternative, the assetmodification module 30 provides a trigger signal and an activationsignal to co-processor regarding the operation.

The asset modification module 30 continues to access the limit table266, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify co-processors 284, and receives resultants fromthe co-processors 480 in accordance with the identifiers. Once themodification of the asset is closed, the asset modification moduleoutputs an asset modification result 262.

FIG. 27 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset (e.g. creating the asset oradding to the asset 484). In this example, the asset modification module30 selects one of the assets 254 to create, or add to, and uses thisinformation to select an operation set limit table 266 from a pluralityof limit tables 256.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information (e.g., a row of informationcorresponds to information regarding an operation, which may be in aparticular sequence order). The asset modification module 30 interpretsthe information to identify one or more evaluation data and retrievesthe corresponding evaluation data 272. The asset modification module 30further interprets the information to identify indicators 274 (e.g.,trigger, detrigger, activate, deactivate, etc.). The asset modificationmodule 30 also interprets the information to identify one or moreco-processors 284 from a pool of co-processors 476.

Having retrieved indicators and identified the co-processor(s), theasset modification module 30 forwards the indicators, the evaluationdata, and operation data to the co-processor(s) 478 for analysis. When atrigger indicator is met, the co-processor triggers the operation andinforms the asset modification module 30 thereof. The asset modificationmodule 30 updates the status of the operation within the limit table276. When an activate indicator is met, the co-processor activates theoperation to create and/or add to the asset and informs the assetmodification module 30 thereof. The asset modification module 30 updatesthe status of the operation within the limit table 276. Upon executionof the operation, the co-processor provides resultants 480 to the assetmodification module 30. Alternatively, the asset modification module 30analyzes the evaluation data 482 to determine the triggering andactivation of an operation. In this alternative, the asset modificationmodule 30 provides a trigger signal and an activation signal toco-processor regarding the operation.

The asset modification module 30 continues to access the limit table266, identify operation data 268, identify evaluation data 272, retrieveindicators 274, identify co-processors 284, and receive resultants fromthe co-processors 480 in accordance with the identifiers to createand/or add to the asset. Once the modification of the asset is closed,the asset modification module 30 outputs an asset modification result262 (e.g., the generated or updated asset).

FIG. 28 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset. In this example, the assetmodification module 30 selects one of the assets to manage and retrievesthe operational data regarding the asset 264 in accordance with aselection process. The asset modification module 30 also selects anoperation set, which has associated therewith an operation set ofco-processors 294. For instance, limit table 1_(—)1 through 1_α haveassociated therewith, operation set 1 of co-processors.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information (e.g., a row of informationcorresponds to information regarding an operation, which may be in aparticular sequence order). The asset modification module 30 interpretsthe information to identify one or more evaluation data and retrievesthe corresponding evaluation data 272. The asset modification module 30further interprets the information to identify indicators 274 (e.g.,trigger, detrigger, activate, deactivate, etc.). The asset modificationmodule 30 also interprets the information to identify one or moreco-processors 284 from the operation set of co-processors 294.

Having retrieved the indicators and identified the co-processor(s), theasset modification module 30 provides the retrieved evaluation data andthe indicators to the identified co-processor(s) for analysis 478. Whena trigger indicator is met, the co-processor triggers an operation andinforms the asset modification module 30 thereof. The asset modificationmodule 30 updates the status of the operation within the limit table276. When an activate indicator is met, the co-processor activates theoperation for execution and informs the asset modification module 30thereof. The asset modification module updates the status of theoperation within the limit table 276. The co-processor executes anactivated operation in accordance with the indicators of the limit tableto produce a partial modification resultant, which it provides to theasset modification module. Alternatively, the asset modification module30 analyzes the evaluation data 482 to determine the triggering andactivation of an operation. In this alternative, the asset modificationmodule 30 provides a trigger signal and an activation signal toco-processor regarding the operation.

The asset modification module 30 continues to access the limit table266, identify operation data 268, identify evaluation data 272, retrieveindicators 274, identify co-processors 284, and receives resultants fromthe co-processors 480 in accordance with the identifiers. Once themodification of the asset is closed, the asset modification moduleoutputs an asset modification result 262.

FIG. 29 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset (e.g. creating the asset oradding to the asset 484). In this example, the asset modification module30 selects one of the assets 254 to create, or add to, and uses thisinformation to select an operation set limit table 266 from a pluralityof limit tables 256, wherein each limit table has associated therewithan operation set of co-processors 294. For instance, limit table 1_(—)1through 1_α have associated therewith, operation set 1 of co-processors.

The asset modification module 30 accesses the selected limit table 268to retrieve one or more rows of information (e.g., a row of informationcorresponds to information regarding an operation, which may be in aparticular sequence order). The asset modification module 30 interpretsthe information to identify one or more evaluation data and retrievesthe corresponding evaluation data 272. The asset modification module 30further interprets the information to identify indicators 274 (e.g.,trigger, detrigger, activate, deactivate, etc.). The asset modificationmodule 30 also interprets the information to identify one or moreco-processors 284 from a pool of co-processors 294.

Having retrieved indicators and identified the co-processor(s), theasset modification module 30 forwards the indicators, the evaluationdata, and operation data to the co-processor(s) for analysis 478. When atrigger indicator is met, the co-processor triggers the operation andinforms the asset modification module 30 thereof. The asset modificationmodule 30 updates the status of the operation within the limit table276. When an activate indicator is met, the co-processor activates theoperation to create and/or add to the asset and informs the assetmodification module 30 thereof. The asset modification module 30 updatesthe status of the operation within the limit table 276. Upon executionof the operation, the co-processor provides resultants 480 to the assetmodification module. Alternatively, the asset modification module 30analyzes the evaluation data 482 to determine the triggering andactivation of an operation. In this alternative, the asset modificationmodule 30 provides a trigger signal and an activation signal toco-processor regarding the operation.

The asset modification module 30 continues to access the limit table266, identify operation data 270, identify evaluation data 272, retrieveindicators 274, identify co-processors 284, and receive resultants fromthe co-processors 480 in accordance with the identifiers to createand/or add to the asset. Once the modification of the asset is closed,the asset modification module 30 outputs an asset modification result262 (e.g., the generated or updated asset).

FIG. 30 is a schematic block diagram of another embodiment of an assetmodification module 30 operably coupled to a co-processor 486. The assetmodification module 30 includes a limit table interface module 296, aplurality of indicator buffers 298-304, memory 306-308, an operationselection module 310, a resultant analysis module 314, a triggeroperation execution module 316, an execute/pause (activate/deactivate)operation execution module 318, and an evaluation data filter 320. Theindicator buffers 298-304 include an operation data indicator buffer298, an evaluation data indicator buffer 300, a trigger indicator buffer302, and an execute/pause (activate/deactivate) indicator buffer 304.The memory 306-308 stores filtered evaluation data 306, operation data308, and may further store the resultant(s) of the operation executionmodule 312. Note that each of the modules may be implemented as asoftware module executable by a processing module, may be implemented asa hard coded module, and/or may be implemented as a firmware module(e.g., a combination of software and hardware). The co-processor 486includes an operation execution module 312.

In an example of operation, the asset modification module 30 identifiesa selected co-processor 486 and interfaces with it. The interfacing maybe locally (e.g., via a computing device bus structure, an applicationprogram interface (API), etc.) or may be remotely (e.g., via a WLANinterface, a LAN interface, a WAN interface, an Internet interface,etc.). Once the asset modification module 30 is operably coupled to theselected co-processor 486, the coupled pair functions in a similarmanner as the asset modification module 30 of FIG. 17. Note that theasset modification module 30 may be operably coupled to multipleselected co-processors 486 at a given time using a multitaskingfunction.

FIGS. 31-33 are a logic diagram of another method for modifying an assetthat may be performed by the asset modification module of FIG. 30. Themethod begins with the asset modification module selecting a limit tablefor a particular asset 488. The method continues with the assetmodification module determining an initial sequence based on one or moreentries of the limit table 490. For example, the asset modificationmodule identifies the operations having a sequence number of 0 and mayfurther identify the operations having the next one or more sequencenumbers. As a specific example, the asset modification module mayidentify the sequence number and ordering from information contained inthe limit table or it may determine it based on the operations,evaluation data being analyzed, and/or the indicators.

The method then branches into three branches. In the first branch, theasset modification module identifies evaluation data indicators for theoperation(s) of the initial sequence (e.g., sequence 0) from the limittable 492. This branch continues with the asset modification moduledetermining whether there is evaluation data to retrieve 494. If yes,the branch continues with the asset modification module retrieving theidentified evaluation data 496. If not, or after the evaluation data isretrieved, this branch continues on FIG. 32.

In the second branch, the asset modification module identifies one ormore co-processors associated with the initial sequence 498. This branchcontinues with the asset modification module retrieving the one or moreoperations. This branch continues with the asset modification moduleretrieving the indicators (trigger, detrigger, activate, and deactivate)for each of the operations 500. This branch then continues on FIG. 32.

In the third branch, the asset modification module identifies operationdata indicators for the selected operations of the initial sequence 502.This branch continues with the asset modification module determinewhether there is at least one operation data indictor (e.g., operationdata ID, data allotment, and/or data operands) to retrieve 504. Forexample, if the operations for the initial sequence are to open an assetmodification process of the limit table, there may not be any operationdata indicators to retrieve. If there is an operation data indicator toretrieve, this branch continues with the asset modification moduleretrieving it 506. Once the asset modification module has, or if thereare no operation data indicators to retrieve, this branch continues onFIG. 32.

On FIG. 32, the second branch continues with the asset modificationmodule determining whether the operation set is complete 508. Forexample, the asset modification module is determining whether the assetmodification process for the selected asset is being closed. If yes, themethod continues with the asset modification module updating 510, ifneeded, status fields in the limit table indicating that the assetmodification process is being closed 512. If the asset modificationprocess is not being closed, the method continues with the assetmodification module determining whether a trigger indicator for anoperation is met 514. If not, the method remains in a loop until theasset modification process is closed or an operation is triggered. Notethat the first branch of FIG. 31 ties into the step of determiningwhether a trigger indicator is met.

When a trigger indicator is met, the method continues with the assetmodification module triggering the co-processor 516. The methodcontinues with the asset modification module determining whether anexecution (or activate) indicator is met 518. If not, the methodbranches back to the step of determining whether a trigger indicator ismet for another operation 514. Also, for the current triggeredco-processor, the method continues with the asset modification moduledetermining whether a detriggering indicator is met 520. If yes, thecurrent co-processor is detriggered 522. If not, the method loops backto determining whether an activate indicator is met 518.

When, for a triggered co-processor, an activate indicator is met, themethod continues by enabling the co-processor to execute the operationto produce a result 524. Note that the first and third branch of FIG. 31tie into the step of enabling the co-processor 524. The method continueswith the asset modification module (via the results analysis module)determining whether the results indicate whether the asset modificationprocess should proceed to the next sequence 526. If yes, the methodcontinues with the asset modification module deactivating theco-processor(s) of the current sequence state and placing them in atriggered state or a detriggered state depending on the next sequence528. For example, if the next sequence could be a repeat of the currentsequence, then the operations are placed in the triggered state. If,however, the next sequence could not be a repeat of the currentsequence, then the operations are placed in a detriggered state.Regardless of the state of the operations are placed in, the methodcontinues on FIG. 33.

If the results of the executed operation do not indicate transitioningto the next sequence state, the method continues with the assetmodification module determining whether a deactivate (or pause)indicator is met for the co-processor 530. If not, the method continuesin three branch back paths. The first branch back path is for theactivated co-processor, which loops back to the step of enabling theco-processor to execute the operation 524. The second branch back pathis for triggered co-processors, which loops back to the step ofdetermining whether an activation indicator is met 518. The third branchback path is for other co-processors of the current sequence that arenot yet triggered, which loops back to the step of determining whether atrigger indicator is met 508.

If a deactivate indicator is met, the method continues with the assetmodification module deactivates the co-processor from executing theoperation 532. The method then continues with the asset modificationmodule determining whether other co-processors are still enabled (e.g.,are still activated) 534. If not, the method continues in two branchback paths. The first branch back path is for triggered co-processors,which loops back to the step of determining whether an activationindicator is met 518. The second branch back path is for otherco-processors of the current sequence that are not yet triggered, whichloops back to the step of determining whether a trigger indicator is met508.

When another co-processor is still activated, the method continues inthree branch back paths. The first branch back path is for the activatedco-processor, which loops back to the step of executing the operation524. The second branch back path is for triggered co-processors, whichloops back to the step of determining whether an activation indicator ismet 518. The third branch back path is for other co-processors of thecurrent sequence that are not yet triggered, which loops back to thestep of determining whether a trigger indicator is met 508.

When the asset modification process is in a transition state, the methodcontinues on FIG. 33 with the asset modification module determiningwhether end the operation set (e.g., close the asset modificationprocess) 536. If yes, the method continues with the asset modificationmodule updating status in the limit table and outputting a resultant, ifany 38.

If the asset modification process is not being closed, the methodcontinues with the asset modification module transitioning the processto the next sequence, which branches into three branches. In the firstbranch, the asset modification module identifies evaluation dataindicators for the operation(s) of the next sequence, or sequences, fromthe limit table 540. This branch continues with the asset modificationmodule determining whether there is evaluation data to retrieve 542. Ifyes, the branch continues with the asset modification module retrievingthe identified evaluation data 544. If not, or after the evaluation datais retrieved, this branch continues on FIG. 32.

In the second branch, the asset modification module identifies one ormore co-processors associated with the next sequence(s) 546. This branchcontinues with the asset modification module retrieving the indicators(trigger, detrigger, activate, and deactivate) for each of theoperations 548. This branch then continues on FIG. 32.

In the third branch, the asset modification module identifies operationdata indicators for the selected operations of the next sequence(s) 550.This branch continues with the asset modification module determinewhether there is at least one operation data indictor (e.g., operationdata ID, data allotment, and/or data operands) to retrieve 552. If thereis an operation data indicator to retrieve, this branch continues withthe asset modification module retrieving it 554. Once the assetmodification module has, or if there are no operation data indicators toretrieve, this branch continues on FIG. 32.

FIG. 34 is a diagram of another example of an operation set limit table212 that includes a plurality of columns and a plurality of rows. Thecolumns correspond to particular aspects of the operation set limittable 212 such as sequence (ordering) 214, operation identifier (ID)216, one or more trigger indicators 218, one or more detriggerindicators 220, evaluation data indicators 222, execute (or activate)indicators 224, pause (or deactivate) indicators 226, operational dataindicators 228, current operation status 230, and execution moduleselection 232. Note that an operation limit table 212 may include moreor less columns (i.e., aspects). For example, the execution moduleselection 232 may be omitted. As another example, the sequence column214 may be omitted. As yet another example, a column or aspect may beadded to indicate configuration information for the evaluation datafilter.

Each row of the operation set limit table 212 corresponds to anidentified operation and its operating conditions. For example, a firstrow below the header includes an entry in the sequence field 214 of 0,an entry in the operation ID 216 field of “A” (e.g., open assetmodification process of an asset), an entry in the trigger indicatorfield 218 of “turn op on signal”, an entry in the de-trigger indicatorfield 220 of “turn op off signal”, an entry in the evaluation data IDfield 234 of “not applicable (n/a)”, an entry in the evaluation datafactors 236 of “n/a”, an entry in the execute indicator field 224 of“upon activation” (e.g., when the turn on signal is detected), an entryin the pause indicator 226 of “upon execution” (e.g., just after it isexecuted), entries in each of the operational data indicators 228 of“n/a”, an entry in the current operation status 230 of “executed”, andan entry in the execution module selection 232 of “A-1” (e.g., useexecution module A-1 to perform the operation).

When operation A has been executed, the next sequenced operations aretriggerable, which are operations B and C since they have a sequencenumber of 1. The entries for operation B include an entry in the triggerindicator field 220 of “dm>=f1(m)”, an entry in the de-trigger indicatorfield 222 of “dm<=f1(m)−f2(m)”, an entry in the evaluation data ID field234 of “dm”, an entry in the evaluation data factors 2236 of “dm sourcetime frame”, an entry in the execute indicator field 224 of“dm>=f1(m)+3”, an entry in the pause indicator 226 of “dm<f2(m)”, anentry in the operation data ID 238 of “Dc”, an entry in the dataallotment 240 of “10%”, an entry in the data operands 242 of “x value”,an entry in the current operation status 230 of “hold”, and an entry inthe execution module 232 selection of “B-1”.

For this row (i.e., operation B), the operation is to be triggered whendm (i.e., the evaluation data set of one or more evaluation data) isequal to or greater than a first function of the evaluation data. Thefirst function may be a mathematical function, a filtering function, alogic function, a mapping function, a statistical function, etc. Forexample, the first function may be to detect when a value of theevaluation data exceeds a threshold, when a slope of the evaluation datais at or above a certain slope, when a pattern of the evaluation data iswithin a given tolerance of a desired pattern. etc.

The operation is to be detriggered when the evaluation data (dm) is lessthan the first function of the evaluation data (f1(m)) minus a secondfunction (f2(m)). The second function may also be a mathematicalfunction, a filtering function, a logic function, a mapping function, astatistical function, etc.

The evaluation data indicators 228 include the evaluation data ID 234 ofdm and an evaluation data factor(s) 236 of a given time frame. Forexample, dm identifies a set of evaluation data that includes one ormore evaluation data and the dm source time frame indicates what time ofthe day the data is to be retrieved.

The execute indicator 224 indicates that the operation is to beactivated for execution when the evaluation data (dm) is equal to orgreater than a first function of the evaluation data plus 3. Theoperation is to be paused (i.e., deactivated) when the evaluation data(dm) is less than the second function on the evaluation data.

The operation data ID 238 identifies operation data (Dc), whichcorresponds to the asset being modified. The data allotment field 240indicates that 10% of the asset is to be subjected to modification byoperation B. The data operand field 242 indicates an operand of thevalue x for operation B to use when executed. The operand may be aninitializing value, a constant for an equation, a unit of measure (e.g.,quantity, pounds, dollars, etc.), a condition for executing theoperation, an execution repetition indication (e.g., how many times theoperation is to be executed), etc.

The current operation status 230 of “hold” indicates that this operationwill not be triggered even if the triggering conditions occur. Anoperation may be put on hold when another operation of the same sequencenumber is triggered or activated. Other operation status includestriggered, activated, executed, waiting (to be triggered), etc.

The third row of the limit table is for operation C, which also has asequence number of 1. Operation C is to be triggered when dn (i.e.,another evaluation data set of one or more evaluation data) is equal toor greater than k, which may be a constant, a slope, a pattern, a trend,etc. Operation C is to be detriggered when the evaluation data (dn) isless than k−2.

The evaluation data indicators 222 include the evaluation data ID 234 ofdn and an evaluation data factor(s) 236 of a given time frame. Forexample, dn identifies a set of evaluation data that includes one ormore evaluation data and the dn source time frame indicates what time ofthe day the data is to be retrieved.

The execute indicator 224 indicates that the operation is to beactivated for execution when the evaluation data (dn) is equal to orgreater than k+2. Operation C is to be paused 226 (i.e., deactivated)when the evaluation data (dn) is less than k.

The operation data ID 238 identifies operation data (Dc), whichcorresponds to the asset being modified. The data allotment field 240indicates that 10% of the asset is to be subjected to modification byoperation C. The data operand field 242 indicates an operand of thevalue x for operation C to use when executed. The current operationstatus of “triggered” indicates that this operation is triggered, butnot yet activated.

From this limited example, a variety of evaluation data can be analyzedfor a variety of characteristics to trigger, de-trigger, activate, andde-activate an operation. For example, the characteristics include, butare not limited to, author, subject matter, values, trends, patterns,slopes, etc.

FIG. 35 is a diagram of another example of a generalized operation setlimit table that includes a sequence column 556, an operation ID column558, a general description of the operation column 560, and ageneralized indicators column 562. The first row is for sequence 0 andidentifies operation A, which is an operation to turn on the assetmodification process when a turn on signal is detected.

The next row of the table is sequence 1 that identifies operations B andC. Each operation, while performing different operations, generallyfunctions to open a position (e.g., for a given asset, subject it tomodification). Either operation B or C is triggered and then executedupon favorable analysis of relevant evaluation data.

The next row of the table is for sequence 2, which identifies operationD. Operation D is a function to extend the position of the asset in afirst manner based on extend asset development type 1 indicators. Forexample, operation D corresponds to buying a certain quantity ofcomponent for a manufactured product based on certain conditions in therelevant evaluation data.

The next row of the table is also for sequence 2, which identifiesoperations E and F. Each of operations E and F, while performingdifferent operations, generally functions to extend the position of theasset in a second manner based on extend asset development type 2indicators. For example, each of operation E and F corresponds to buyingdifferent quantities of component for a manufactured product based oncertain conditions in the relevant evaluation data. As a more specificexample, operation D may be to buy x quantity when inventory is runninglow regardless of price from any vendor; operation E may be to buy zquantity of the component when the price is below a certain thresholdfrom a specific vendor; and operation F may be to buy y quantity of thecomponent when the price is below a certain threshold for a set ofvendors.

The next row of the table is for sequence 3, which identifies operationsG and H. Each of operations G and H, while performing differentoperations, generally function to reduce the position of the asset in afirst manner based on reduced asset development type 1 indicators. Forexample, each of operations G and H corresponds to consuming a componentin the manufacture of a product. As a more specific example, operation Gmay be to use k quantity of the component when the evaluation dataindicates a certain level of a market condition for the product; andoperation H may be use m quantity of the component when the evaluationdata indicates a different level of the market condition for theproduct.

The next row of the table is also for sequence 3, which identifiesoperations K and L. Each of operations K and L, while performingdifferent operations, generally function to reduce the position of theasset in a second manner based on reduced asset development type 2indicators. For example, each of operations K and L corresponds toselling an abundance of a component used in the manufacture of aproduct. As a more specific example, operation K may be to sell aquantity of the component when the evaluation data indicates aparticular market condition for the product; and operation L may be sella different quantity of the component when the evaluation data indicatesa different market condition for the product.

The next row of the table is for sequence 4, which identifies operationsM, N, and O. Each of operations M, N, and O, while performing differentoperations, generally function to close the position of the asset whenthe evaluation data is unfavorable. For example, operation M closes theasset modification process when the evaluation data is unfavorable in afirst manner; operation N closes the asset modification process when theevaluation data is unfavorable in a second manner; and operation Ocloses the asset modification process when the evaluation data isunfavorable in a third manner.

The last row of the table is for sequence 5, which identifies operationP. Operation P functions to turn off the asset modification process whena turn off signal is detected.

FIG. 36 is a diagram of an example of operation sequencing of the limittable of FIG. 35.

In this diagram, the processing starts in sequence 0 with operation Aengaged to detect the turn on signal. Until the turn on signal isdetected, the asset modification process remain is sequence 0 (e.g., theasset modification process is off). Once the turn on signal is detected,the sequencing transitions to sequence 1, which is to open a positionfor an asset. In sequence 1, operations B and C are engaged to open aposition as discussed above.

Once a position for an asset is opened, the asset modification processis in a transition state and can transition from sequence 1 to sequence2, 3, or 4 based on the indicators. For example, if the extend assetdevelopment per type 1 indicators are detected in the evaluation data,the process transitions to sequence 1 and operation D is engaged. Asanother example, if the extend asset development per type 2 indicatorsare detected in the evaluation data, the process transitions to sequence1 and operations E and/or F are engaged. As yet another example, if thereduced asset development per type 1 indicators are detected in theevaluation data, the process transitions to sequence 3 and operations Gand H are engaged. As yet another example, if the reduced assetdevelopment per type 2 indicators are detected in the evaluation data,the process transitions to sequence 3 and operations K and L areengaged. As a further example, if the analysis of the evaluation data isunfavorable (e.g., for a given time period), the process transitions tosequence 4 and operations M, N, and O are engaged to close the positionfor the asset.

When the process is in sequence 1, it may transition to sequence 2, 3,or 4. When the process is in sequence 2, it may transition to sequence3, 4 or it may transition back to sequence 2. When the process is insequence 3, it may transition to sequence 2, 4, or it may transitionback to sequence 3. When the process is in sequence 4, it may transitionto sequence 1 or to 5. The various transitions are based on analysis ofthe evaluation data in light of the various indicators.

FIG. 37 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset in accordance with the limittable of FIG. 35 and sequence diagram of FIG. 36. In this example, theasset modification module 30 has selected one of the assets (e.g.,asset 1) to manage and retrieves the operational data regarding theasset in accordance with a selection process (e.g., user selection,automated determination process, default selection, etc.).

The asset modification module 30 then selects an operation set limittable from a plurality of limit tables 256 (e.g., step 1 for sequence0). In this example, asset 1 has limit tables 1_(—)1 through 1_αavailable and the asset modification module 30 selects one of them basedon attributes and/or factors of the limit tables in accordance with userpreferences and/or a calculated preference. For this example, the limittable of FIG. 35 is selected.

The asset modification module accesses the selected limit table toretrieve the first row of information (e.g., the sequence 0 row). Theasset modification module 30 interprets the information to identifyindicators (e.g., trigger, detrigger, activate, deactivate, etc.). Theasset modification module 30 also interprets the information toidentify, and retrieve, operation A from a pool of operations 258 (e.g.steps 2 & 3 for sequence 0).

Having retrieved indicators and operation A, the asset modificationmodule 30 waits for the trigger and the activate indicators to be met(e.g., detect activation of a turn on signal). When an activateindicator is met, the asset modification module 30 executes operation A(e.g., step 4 of sequence 0). The asset modification module 30 thenupdates the status of the limit table (e.g., indicating that operation Ahas been executed) and places the asset modification process in asequence transition state (e.g., step 5 of sequence 0).

FIG. 38 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset transitioning to sequence 1 inaccordance with the limit table of FIG. 35 and sequence diagram of FIG.36. The processing in sequence 1 begins with the asset modificationmodule 30 accessing the limit table (e.g., step 1 of sequence 1). Fromthe limit table, the asset modification module 30 identifies operationsto retrieve (e.g., step 2 of sequence 1); identify operation data (e.g.,step 3 of sequence 1); identify evaluation data (e.g., step 3 ofsequence 1); and the identify indicators for the identified operations(e.g., step 3 of sequence 1).

In this example, the asset modification module 30 identified operationsB, C, and P from the limit table (step 2). With reference to FIG. 36,the sequencing can transition from sequence 0 to sequence 1 or tosequence 5 after successful execution of operation A of sequence 0. Assuch, operations B and C are operations of sequence 1 and operation P isthe operation of sequence 5. In other words, once operation A isexecuted to turn on the asset modification process, the process waitsfor either operation B or C to be executed to open the asset formodification or for operation P to be executed to turn off the assetmodification process.

For step 3 of sequence 1, the asset modification module 30 identifiesthe operation data indicators for the asset (e.g., the asset ID, thedata allotment amount, and data operands, if any). In addition, theasset modification module 30 identifies evaluation data for analysis andidentifies the other indicators (e.g., trigger indicator, de-triggerindicator, execute indicator, and pause indicator).

The processing within sequence 1 continues at step 4 where the assetmodification module 30 retrieves the identified evaluation data and theidentified operation data (if any). The asset modification moduleanalyzes the retrieved evaluation data in light of the indicators. Whena trigger indicator is met for one of the operations, the operation istriggered and the status of the operation within the limit table isupdated accordingly. When an activate indicator is met for a triggeredoperation, the operation is activated for execution and the status ofthe operation within the limit table is updated accordingly. The assetmodification module 30 executes the activated operation in accordancewith the indicators of the limit table to produce a partial modificationresultant (e.g., step 5 of sequence 1).

Depending on which operation is triggered, activated, and executed, theasset modification process transitions to a next sequence. For instance,if operation P is executed, the asset modification process is turnedoff. If, however, operation B or C is executed, the asset modificationprocess is in a transition state from which it could transition tosequence 2, 3, or 4.

FIG. 39 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset transitioning from sequence 1to sequence 2, 3, or 4 in accordance with the limit table of FIG. 35 andsequence diagram of FIG. 36. The sequence transitioning processingbegins with the asset modification module 30 accessing the limit table(e.g., step 1 of transitioning from sequence 1). From the limit table,the asset modification module 30 identifies operations to retrieve(e.g., step 2); identify operation data (e.g., step 3); identifyevaluation data (e.g., step 3); and the identify indicators for theidentified operations (e.g., step 3).

In this example, the asset modification module 30 identified operationsD-O from the limit table (step 2). With reference to FIG. 36, thesequencing can transition from sequence 1 to sequence 2, 3, or 4 aftersuccessful execution of operation B or C of sequence 1. Morespecifically, operations D-F are operations of sequence 2, operations G,H, K, and L are operations of sequence 3, and operations M, N, and O areoperations of sequence 4. In other words, once operations B or C isexecuted to open the asset for modification, the process waits for oneof operations D-O to be triggered to transition to the next sequence.

For step 3 of transitioning from sequence 1, the asset modificationmodule identifies the operation data indicators for the asset (e.g., theasset ID, the data allotment amount, and data operands, if any). Inaddition, the asset modification module identifies evaluation data foranalysis and identifies the other indicators (e.g., trigger indicator,de-trigger indicator, execute indicator, and pause indicator).

The processing of transitioning from sequence 1 continues at step 4where the asset modification module 30 retrieves the identifiedevaluation data and the identified operation data (if any). The assetmodification module 30 analyzes the retrieved evaluation data in lightof the indicators. When a trigger indicator is met for one of theoperations, the operation is triggered and the status of the operationwithin the limit table is updated accordingly. When an activateindicator is met for a triggered operation, the operation is activatedfor execution and the status of the operation within the limit table isupdated accordingly. The asset modification module 30 executes theactivated operation in accordance with the indicators of the limit tableto produce a partial modification resultant (e.g., step 5).

Depending on which operation is triggered, activated, and executed, theasset modification process transitions to a next sequence. For instance,if one of operations D-F is triggered, the asset modification processtransitions to sequence 2, where the asset is extended. If, however, oneof operations G, H, K, or L is triggered, the asset modification processtransitions to sequence 3, where the asset is reduced. Alternatively, ifone of operations M, N, or I is triggered, the asset modificationprocess transitions to sequence 4, where the asset modification processfor the asset is closed.

FIG. 40 is a schematic block diagram of another example of an assetmodification module 30 modifying an asset in sequence 4 in accordancewith the limit table of FIG. 35 and sequence diagram of FIG. 36. Withthe asset modification process in sequence 4, it can only transition tosequence 1 (open an asset for modification) or to sequence 5 (turn offthe asset modification process).

In this sequence state, the asset modification module 30 identifiedoperations B, C, and P from the limit table. Depending on whichoperation is triggered, activated, and executed, the asset modificationprocess transitions to a next sequence. For instance, if operation P isexecuted, the asset modification process is turned off. If, however,operation B or C is executed, the asset modification process is in atransition state for the asset or for another asset from which it couldtransition to sequence 2, 3, or 4.

FIGS. 41-42 are a diagram of another example of an operation set limittable for financial portfolio management. The operation set limit tableincludes a plurality of columns and a plurality of rows. A row of thetable corresponds to information for a particular operation and thecolumns corresponding to the specific information of the operation. Thecolumns include a sequence field, an evaluation type field (e.g.,evaluation data ID and/or evaluation data factors), a notes field(optional and for reference purposes), an activation trigger (e.g.,trigger), an execution trigger (e.g., activate for execution) as shownin FIG. 41 and includes action field (e.g., operation ID), a % tradeallotment field, a trigger reference field (e.g., data operand), astatus field, and an additional actions field as shown in FIG. 42. Notethat the first two columns in FIG. 42 are the same columns as in FIG. 41and are shown for ease of reference.

FIGS. 43-47 illustrate various examples of performing an assetmodification process in accordance with the limit table of FIGS. 41 and42. FIG. 43 includes two graphs of evaluation data being analyzed forthe asset. The top graph illustrates a line format of the current price,which plots the current price of the asset in a line format over time,and the bottom graph illustrates the current price in a candlestickformat, which plots a candlestick format of the current price over time.

The asset modification module analyzes the evaluation data in light ofthe sequence one operations to trigger (i.e., enable) an assetmodification process for the asset. The sequence one operations includean open initial signal detection, an open initial position based onmoving average, and an open initial position based on money flow. Forthe open initial position based on moving average operation, the assetmodification process for an initial position is triggered (e.g.,enabled) when the distance from the moving average-to-price crossing isat a level indicated by the trigger and activation indicators. In thisexample, the trigger indicator is 10 and the activation indicator is 0,indicating that when the trigger indicator is met, the operation istriggered and activated.

Once the asset modification process is enabled for an initial position,the asset modification module analyzes the evaluation data to determineif an operation to open an initial position is triggered (e.g., asequence two operation that occurred after one of the sequence 1operations is executed). For the sequence 1 operation, the limit tableincludes a trigger indicator of 0 from base level (e.g., the initialprice), an execution indicator of 0, on operation ID of “open firstposition”, a data allotment indicator of 10%, and a status. Since thetrigger and execution indicators are identical, once the triggercondition is met, the operation of “open first position” with a 10%allotment at the current (or initial) price is executed.

The asset modification module continues to analyze the evaluation datafor another operation to be triggered. At this point in the execution ofthe asset modification process, it may continue at any sequence level.In the example of FIG. 43, the analysis of the candlestick format of thecurrent price is rising and triggers the “open limit 2 tops operation”when, as indicated by the trigger indicator, it is 10 units (e.g.,percent, dollars, etc.) higher than the initial price (e.g., dataoperand). Since the activation trigger is 0, it indicates that theoperation is to be activated (i.e., executed) when the trigger conditionis met. As such, at this point, the asset modification module executesthe operation to purchase 25% more of the asset, which now has 35% open.

As time continues, the “close limit 3 tops” operation is triggered whenthe candlestick format of the current price drops. The assetmodification module executes the operation to sell 25% of the asset atthe current price, leaving 10% of the asset open. When the candlestickformat of the current price again rises, the asset modification moduleexecutes the “open limit 2 tops operation” to purchase 25% more of theasset, which now has 35% open.

As time continues, the “open remaining” operation is triggered when theline format of the current price reaches 1.2*(initial price). Since theactivation indicator is 0 for this operation, the asset modificationmodule executes the operation upon its triggering to open 100% of theasset, which, for this stage of the example, equates to purchasing 65%of the asset at the then current price. The example continues with theasset modification module executing the “close limit 3 tops” operation acouple of times perform executing a “close all positions” operationbased on a close all signal.

FIG. 44 illustrates another example of asset modification using the lineformat of the current price. In this example, after the initial positionis opened, the current price drops from the initial price. The assetmodification module analyzes the data for a triggering condition to bemet. When the current price is down 30 from the initial price, the “openlimit” operation is triggered (e.g., trigger indicator is −30). For thisoperation, the activation indicator is 20, meaning that the currentprice needs to rise 20 from the price at which the operation wastriggered before it is executed. When this occurs, the assetmodification module executes the “open limit” operation to purchase anadditional 20% of the asset.

FIG. 45 illustrates another example of asset modification using the lineformat of the current price and an average position price. In thisexample, the analysis of the data is occurring some time after theinitial position is opened (e.g., at time t1) where there is 50% of theasset open. The asset modification module analyzes the data for atriggering condition to be met. When the average position price is up 10from a given reference point (e.g., the initial price), the “closedestination stop” operation is triggered (e.g., trigger indicator is10). For this operation, the activation indicator is 20 (e.g., −20 for aclose operation), meaning that the average position price needs to drop20 from the average position price at which the operation was triggeredbefore it is executed. When this occurs, the asset modification moduleexecutes the “close destination stop” operation to sell all openpositions of the asset, which is the 50% that was open in this example.

FIG. 46 illustrates another example of asset modification using the lineformat of the current price and an average position price. In thisexample, the analysis of the data is occurring some time after theinitial position is opened (e.g., at time t1) where there is 50% of theasset open. The asset modification module analyzes the data for atriggering condition to be met. When the average position price is up 40from a given reference point (e.g., the initial price), the “close limitfloor” operation is triggered (e.g., trigger indicator is 40). For thisoperation, the activation indicator is 20 (e.g., −20 for a closeoperation), meaning that the average position price needs to drop 20from the average position price at which the operation was triggeredbefore it is executed. When this occurs, the asset modification moduleexecutes the “close limit floor” operation to sell 50% of the openpositions of the asset, which leaves 25% of the asset open.

FIG. 47 illustrates another example of asset modification using the lineformat of the current price and an average position price. In thisexample, the analysis of the data is occurring some time after theinitial position is opened (e.g., at time t1) where there is 50% of theasset open. The asset modification module analyzes the data for atriggering condition to be met. When the average position price is up 20from a given reference point (e.g., the initial price), the “closelimit” operation is triggered (e.g., trigger indicator is 20). For thisoperation, the activation indicator is 5 (e.g., −5 for a closeoperation), meaning that the average position price needs to drop 5 fromthe average position price at which the operation was triggered beforeit is executed. When this occurs, the asset modification module executesthe “close limit” operation to sell 20% of the open positions of theasset, which leaves 40% of the asset open.

FIG. 48 is a diagram of an example of interoperations of multipleoperation set limit tables. In this example, a limit table may include arow of information that points to another limit table, to an operationto determine an operation 564, and/or an operation to determine anotherlimit table 566. The limit table may further include rows of informationthat identify particular operations as previously discussed. Forexample, limit table 1 includes a row of information that points to anoperation pointing operation 564. The row of information includesindicators (e.g., evaluation data, operation data, trigger, andactivation) that enable the operation pointing operation 564 to identifya particular result operation. As a specific example, assume that resultoperation 1 is an operation to purchase a component from a particularvendor and operation m is an operation to purchase the component fromanother vendor. The operation pointing operation 564 executes itsoperation in light of the indicators to determine whether one or both ofthe results operations 1 and m should be selected.

As another example, limit table 1 includes a row of information thatpoints to a limit table (LT) pointing operation 566. The row ofinformation includes indicators (e.g., evaluation data, operation data,trigger, and activation) that enable the LT pointing operation 566 toidentify a particular limit table. As a specific example, assume thatlimit table 2 is regarding purchasing a component based on a first setof evaluation data and limit table 3 is regarding purchasing a componentbased on a second set of evaluation data. The LT pointing operation 566executes its operation in light of the indicators to determine whetherone or both of the limit tables 2 and 3 should be selected. Note thatpart of the operation may be to test each limit table in light of pastevaluation data to determine which one to select.

As yet another example, limit table 1 includes a row of information thatpoints to a limit table (LT). The row of information includes indicators(e.g., evaluation data, operation data, trigger, and activation) thatenable limit table 1 to identify a particular limit table. As a specificexample, assume that limit table 2 is regarding purchasing a componentbased on a first set of evaluation data and limit table 3 is regardingpurchasing a component based on a second set of evaluation data. Limittable 1 executes the appropriate operation in light of the indicators todetermine whether one or both of the limit tables 2 and 3 should beselected.

FIG. 49 is a diagram of an example of an asset modification moduleimplementing an asset modification process via an operation set limittable that includes rows of information for six operations (A-F). Atrigger indicator analysis module 568 and an activation indicatoranalysis module 570 of the asset modification module are shown. Theasset modification module may include other modules as previouslydiscussed. Each of the operations may be in one of three states:inactive state 572 (i.e., not triggered), triggered 574, and executing576.

In this example, at time tx, operations A, B, and C are in the triggeredstate 574 and operations D, E, and F are in the inactive state 572. Inaddition, the trigger indicator analysis module 568 is analyzing theevaluation data 578 to determine whether to trigger one or more ofoperations D, E, and F and the activation indicator analysis module 570is analyzing the evaluation data 578 to determine whether to activatefor execution one or more of operations A, B, and C.

FIG. 50 is a continuation of the example of FIG. 49 at time tx+1. Atthis point in time, the activation indicator analysis module 570determines that the evaluation data 578 is exhibiting a condition thatcorresponds to an activation indicator for operation A. As such,operation A is activated for execution on the corresponding operationdata 580. Once operation A has performs its operation and outputs itsresult, it is returned to a triggered state 574.

FIG. 51 is a continuation of the example of FIGS. 49 & 50 at time tx+2.At this point in time, the trigger indicator analysis module 570determines that the evaluation data 578 is exhibiting a condition thatcorresponds to a detriggering indicator for operation A. As such,operation A is detriggered and placed in the inactive state 572.

FIG. 52 is a continuation of the example of FIGS. 49-51 at time tx+3. Atthis point in time, each of the trigger and activation indicatoranalysis modules 568-570 determines that the evaluation data 578 isexhibiting a condition that corresponds to a trigger indicator and anactivation indicator, respectively, for operation D. As such, operationD is activated for execution on the corresponding operation data 580.Once operation D has performs its operation and outputs its result, itis returned to a triggered state 574.

FIG. 53 is a diagram of another example of an operation set limit table212 that includes a plurality of columns and rows of informationcorresponding to operations. This limit table is similar to the limittables of FIGS. 9 and 34, with the exception that it does not include asequence column. As such, when the asset modification module isperforming an asset modification process in accordance with the limittable, the trigger and activation indicators for each operation isanalyzed with reference to the appropriate evaluation data. Thus, anyone or more of the operations may be triggered and/or activated at agiven time without reference to what other operations may or may not betriggered and/or activated. Note that the limit table 212 may includeone or more rows of information that point to another limit table, to anoperation pointing operation, and/or to a limit table pointing operationas discussed with reference to FIG. 48.

FIG. 54 is a diagram of an example of differing philosophies whenbuilding an operation set limit table. In this diagram, the horizontalaxis corresponds to an approach to analyzing the evaluation data 582,where, closer to the origin, corresponds to a more aggressive approach.The positive vertical axis corresponds to a favorable analysis 584(e.g., the evaluation data is indicating conditions are favorable toincrease an asset), where, the further from the origin, the morefavorable. The negative vertical axis corresponds to an unfavorableanalysis 586 e.g., the evaluation data is indicating conditions areunfavorable to increase an asset), where, the further from the origin,the more unfavorable.

In general, the analysis of the evaluation data 582 includes, but is notlimited to, detecting predictable patterns, trends, factors, values,etc. of the current evaluation data. The level of favorability (orunfavorability) is how closely the detected patterns, trends, factors,values, etc. match past patterns, trends, factor values, etc., thepredictability of the asset modification outcome therefrom, and thelikelihood that evaluation data will continue to follow past trends,patterns, factors, values, etc. For example, when the analysis yieldspatterns, trends, etc. that don't particular match the predictablepatterns, trends, etc., the favorability may be indeterminate 588. Theanalysis may also be indeterminate 588 if the pattern, trend, etc. arecorresponding to predictable patterns, trends, etc., but the outcome ofasset modification from these patterns is mixed (e.g., 50% of the time,the asset grows, 30% of the time the asset decreases, and 20% of thetime the asset remains relatively constant). The analysis may further beindeterminate if the pattern, trend, etc. are corresponding topredictable patterns, trends, etc., but the likelihood that theevaluation data will continue to follow the trends, patterns, etc. ismixed (e.g., some times it does continue and other times it does not).

As the ability to calculate a favorable or unfavorable analysis, themore the curve moves from the origin (e.g., the indeterminate state).Even though the analysis may be indeterminate, a limit table may includeone or more operations to modify an asset in this state of analysis. Theamount of an asset to expose to modification and what indicators to setfor triggering and activating the modification process will varydepending on the aggressiveness of the approach being taken. The moreaggressive the approach, the more of the asset will be exposed and thelower the thresholds for the indicators will be.

When the analysis is favorable, the level of favorability 584 may beused to determine how much of the asset to expose for modification andthe threshold for the indicators. For instance, when the pattern, trend,etc. matching is good, the predictability is good, and the likelihood ofcontinuing is good, then may want to take an aggressive approach andsubject a majority of the asset to modification. Conversely, when theanalysis is unfavorable 586, may want to take a less aggressive approachto minimize the asset to modification.

FIG. 55 is a diagram of an example of historical evaluation data table590 for building an operation set limit table. The table includes fieldsfor evaluation data ID 592, characteristics 594, recognition filterparameters 596, frequency 598, frequency deviation 600, factors 602,subsequent characteristics 604, and probability of subsequentcharacteristics 606. Each row corresponds to a particular characteristicof one or more evaluation data. An evaluation data may be identified inmultiple rows for different characteristics.

For example, evaluation data 1 is being analyzed for pattern a using twodifferent recognition filter parameters. Using these parameters, thepattern occurs about three times per day for the last month with a dailydeviation of 0.65 times per day. For parameter set 1, the factorsinclude that this pattern is not preceded by pattern β. Under theseconditions, trend B will occur within a given time frame (e.g., factionsof a second, seconds, minutes, hours, days, etc.) with a probability of67% (e.g., for the number of times these conditions have been monitors,trend B follows pattern a within the time frame 67% of the time). Forparameter set 2, the factors include that this pattern is preceded bypattern β. Under these conditions, trend A will occur nearly immediately(e.g., factions of a second, seconds, etc.) with a probability of 80%.

As another example, evaluation data 2 is being analyzed for pattern φ,trend A, and a three-month minimum value using different parameters. Forthe time the evaluation data has been analyzed, which could be days,weeks, months, etc., pattern φ occurs about 7 times per day for the lasttwo weeks and trend A has occurred once per day for the last five weeks.Pattern φ has a daily deviation of 3.5 and trend A has a daily deviationof 0.15. The factor for pattern φ is that current evaluation data has toexceed a value of M; there are no factors for trend A. When theseconditions are met for pattern φ, pattern Ψ is the subsequent patternabout 40% of the time, pattern λ is the subsequent pattern about 30% ofthe time, and the remaining 30% of the time there is no discerniblecharacteristic. When the three-month minimum value is detected, trend Coccurs within 2 days about 80% of the time.

The last entry in the table identifies evaluation data 4, 5, and 6 witha characteristic of function 1. The function may be a logic function, amathematical function, a comparative function, a translation function, ascaling function, a mapping function, a filtering function, acombination thereof, etc. The analysis provided favorable results with afrequency of 2 times per day for the last 8 weeks with a daily deviationof 0.35. There are no factors and the subsequent characteristic ispattern Ω of evaluation data 4 within K minutes at a probability of 35%.In practice, the evaluation data being analyzed, the characteristics,the parameters, the frequency, the frequency deviation, factors,subsequent characteristics, and probability may be in an almost endlesscombination and range of results.

FIG. 56 is a schematic block diagram of an embodiment of an evaluationfilter of an asset modification module that may be used to build a limittable. The evaluation filter includes a buffer 608, a time scaler 610,an amplitude scaler 612, a recognition filter 614, and an analyzer 616.The buffer 608 is of sufficient size to store a sample set of theevaluation data being analyzed. The time scaler 610 and amplitude scaler612 function in accordance with the recognition filter parameters 618 toscale the samples of the evaluation data to fit within a sample windowof a reference pattern 624. FIG. 57 is a diagram of an example of areference pattern 624 that is within a sample window having a time frameof T0 and an amplitude range of A0.

The recognition filter 614, which may be a matching filter, aconvolution filter, etc., filters the scaled evaluation data samples forone or more recognizable characteristics (e.g., trends, patterns,values, function result, etc.) in light of the recognition filterparameters 618 (e.g., a reference pattern, a reference trend, areference value, a reference function result, etc.). The analyzer 616analyzes the filters output for how closely the evaluation data (e.g.,as shown in FIG. 58) matches the reference characteristic to produce arecognition output 622. The more closely the filtered evaluation datamatches the reference pattern 624, the more favorable the analysis. Inaddition, the analyzer 616 may provide feedback 620 to adjust the timeand/or amplitude scaling to adjust the evaluation data sample 626.

FIG. 59 is a schematic block diagram of an example of operation of anevaluation data recognition filter 614. In this example, the evaluationdata sample is at various rates and/or magnitudes 628. As the timewindow 630 scans the evaluation data sample, the recognition filter 614is comparing it to a reference characteristic (e.g., a reference pattern624).

FIG. 60 illustrates the output of the recognition filter 632 as the timewindow 630 scans the evaluation data sample. For a majority of thesample time window 630, the output of the filter is low. The analyzer616 analyzes the output with reference to thresholds (e.g., no match,possible match, probable match, likely match, etc.). When the output isbelow the no match threshold 634, the analyzer 616 provides no feedbackand keeps the time window 630 scanning the evaluation data sample.

When the output of the filter is above the no match threshold 634, theanalyzer 616 has some decisions to make. First, it decides what otherthresholds the output exceeds. If it exceeds the likely match threshold640, the analyzer marks the current time window position of theevaluation data sample to indicate a likely match to the referencecharacteristic. In this instance, the analyzer 616 continues analyzingthe output of the filter for the reference characteristic to occuragain. Note that the filter may be analyzing the evaluation data withrespect to multiple reference characteristics. In this instance, whenthe output exceeds the no match threshold, the analyzer determineswhether another threshold is exceeded and for what referencecharacteristic.

When the output of the filter is at a level above the no match threshold634 and below the likely match threshold 640, the analyzer 616 mayprovide feedback to adjust the evaluation data sample to determine if abetter indication of a match can be achieved. If yes, the analyzer 616includes that in the recognition output 632. If not, the analyzer 616includes the initial, or more favorable, match outcome as therecognition output.

FIGS. 61-63 are a logic diagram of a method for building a limit tablethat may be executed by recognition filter module. The method beginswith the module providing a sliding time window of an evaluation datasample to the recognition filter without adjustment to the time scale orthe amplitude scale 642. The method continues with the module providingrecognition filter parameters regarding a reference characteristic tothe recognition filter 644. The method continues with the moduleanalyzing the recognition filter output 646.

The method branches in accordance with the analysis of the recognitionfilter output 648. When the match indicator indicates a likely match,the evaluation data history table is updated 650. When the matchindicator indicates no match, the method continues with the moduledetermining whether the time and/or amplitude scaling variations havebeen exhausted 652. For example, may allow for scaling from 50% to 200%of the original evaluation data sample. If the variations are exhausted,the method is ended for the current analysis, but repeats for furtheranalysis of evaluation data samples.

If the variations are not exhausted, the method continues with themodule adjusting the time and/or amplitude of the evaluation data sample654. The method continues with the module providing the adjustedevaluation data sample to the recognition filter and the method repeatsas shown 656.

When the match indicator indicates a possible match, the methodcontinues in FIG. 62 where the module adjusts the time and/or amplitudeof the evaluation data sample 658. The method continues with the moduleproviding the adjusted evaluation data sample to the recognition filter660. The method continues with the module providing a sliding timewindow to the recognition filter for the adjusted evaluation datasample. The method continues with the module providing recognitionfilter parameters regarding a reference characteristic to therecognition filter. The method continues with the module analyzing therecognition filter output 662.

The method continues with the module analyzing the output of therecognition filter for a better match 664. If a better match is notindicated, the method continues with the module determining whether thetime and/or amplitude scaling variations have been exhausted 666. If thevariations are exhausted, the module updates the historical evaluationdata table based on the results of the possible match 668. If thevariations are not exhausted, the method continues with the moduleadjusting the time and/or amplitude of the evaluation data sample 670.The method continues with the module providing the adjusted evaluationdata sample to the recognition filter and the method repeats as shown.

When the analysis indicates a better match, the method continues withdetermining whether the better match is a likely match 672. If yes, themethod continues with the module updating the historical evaluation datatable based on the results of the likely match 674. If the better matchis not a likely match, the method continues with the module determiningwhether the better match is a probable match 676. If not, the methodrepeats as shown. If yes, the method continues on FIG. 63.

In FIG. 63, when the match indicator indicates a probably match, themethod continues with the module adjusting the time and/or amplitude ofthe evaluation data sample 678. The method continues with the moduleproviding the adjusted evaluation data sample to the recognition filter.The method continues with the module providing a sliding time window tothe recognition filter for the adjusted evaluation data sample. Themethod continues with the module providing recognition filter parametersregarding a reference characteristic to the recognition filter 680. Themethod continues with the module analyzing the recognition filter output682.

The method continues with the module analyzing the output of therecognition filter for a better match 684. If a better match is notindicated, the method continues with the module determining whether thetime and/or amplitude scaling variations have been exhausted 686. If thevariations are exhausted, the module updates the historical evaluationdata table based on the results of the probable match 688. If thevariations are not exhausted, the method continues with the moduleadjusting the time and/or amplitude of the evaluation data sample 690.The method continues with the module providing the adjusted evaluationdata sample to the recognition filter and the method repeats as shown.

When the analysis indicates a better match, the method continues withdetermining whether the better match is a likely match 692. If yes, themethod continues with the module updating the historical evaluation datatable based on the results of the likely match 694. If the better matchis not a likely match, the method repeats as shown.

FIG. 64 is a logic diagram of another method that may be executed by anasset modification module or other processing module to build a limittable. The method begins with the module obtaining limit table (LT)configuration criteria, which relates to desired attributes of the limittable 696. The attributes include, but are not limited to, risk level,evaluation data relevancy, asset modification philosophy, reliabilitylevel (e.g., proven, unproven, works some times, etc.), favorableevaluation data patterns and/or trends, performance information, etc.

The method continues with the module determining asset modificationobjectives based on the LT configuration criteria 698. The assetmodification objectives include, but are not limited to, grow asset,gather information, manage asset use, manage asset distribution,identify use opportunities, identify sales opportunities, and identifyacquisition opportunities.

The method continues with the module selecting evaluation data ofinterest based on the asset modification objectives 700. The methodcontinues with the module determining evaluation data factors (e.g.,type of asset, modification timing (e.g., time of day, inventorydepletion, etc.), evaluation data sources of interest availability,evaluation data analysis (e.g., pattern mapping, trend detection, valuethresholds, comparative analysis, etc.)) 702. The method continues withthe module selecting characteristics of the selected evaluation data tomonitor based on the evaluation data and the asset modificationobjectives 704. These three steps may be based on default settings,starting with a large number of evaluation data and narrowing via thesesteps, user inputs, a look up table, subscription to evaluation datasources, trial and error, etc.

The method branches into three parallel branches. In the parallelbranches, the module determines activation indicators based on theselected characteristics 706, determines trigger indicators based on theselected characteristics 708, and determines operation data indictorsbased on the asset modification objectives and the selectedcharacteristics 710.

The method continues with the module creating a limit table entry basedon the foregoing 712. The method continues with the module determiningwhether to select another characteristic 714. If yes, the method repeatsas shown. If not, the method is complete.

FIG. 65 is a schematic block diagram of an example of an assembly lineof building a product 716. In this example, the product 716 is comprisedof a plurality of components (1, . . . , X, Y, . . . , and β). At leastsome of the components are comprised of subcomponents. For example,component 1 is comprised of sub-components 1-1 through 1-x, which istypically manufactured by a different entity than the entitymanufacturing the product. In addition, some of the sub-components maybe comprised of further sub-components. For example, sub-component 1-1is comprised of further sub-components 1-1-1 through 1-1-y.

The availability of a component, sub-component, or furthersub-components affects an inventory philosophy for manufacturing theproduct. For example, if one sub-component or further sub-componentbecomes in short supply, the production of the product may be adverselyaffected. As such, it would behoove the manufacturer to monitorevaluation data that affects the availability of its components,sub-components, and further sub-components. The evaluation data mayinclude pricing information, component availability, raw materialsavailability, raw material supply issues, shipping requirements and/orconstraints, assembly requirement information (e.g., timing, complexity,labor involvement, etc.), import/export regulations, political issues,labor issues, weather, component and/or sub-component demand, productmarketing information, etc.

FIG. 66 is a diagram of an example of limit tables 718 for inventorymanagement of an assembly line of building a product of FIG. 65. Asshown, a plurality of limit tables 718 may be created for managinginventory for the product, each of which includes various operationsthat, when executed, produces product operation data analysis 720, whichmay include, for a given time frame (e.g., a day, a week, a month,etc.), buy more of one or more components, buy less of one or morecomponents, buy at status quo for one or more components, useralternative suppliers for one or more components, increase inventory ofone or more components for the next time frame, decrease inventory ofone or more components for the next time frame, timing for placingorders for one or more components, quantity per order, determineshipping options for one or more components, etc.

The execution of a selected limit table 718 is based on the evaluationdata regarding the product 722 and the operation data analysis for atleast some of the components. In addition, the selection of a limittable 718 may be based on the operation data analysis of one or more ofthe components. Executing the operations of a selected limit table 718produces the component operation data analysis for a component.

FIG. 67 is a diagram of an example of limit tables 718 for inventorymanagement of a component of a product of FIG. 65. This exampleillustrates the tiers of operation data analysis for component 1 beingbuilt on the operation data analysis of its sub-components, which isbuilt on the operation data analysis of its sub-components. By using g atiered analysis approach to inventory management, inventory supplies canbe adjusted based on issues that affect the availability and pricing ofcomponents. For example, historical analysis of politics in a particularcountry shows that, when a particular political issue arises, theavailability of raw materials for a sub-component is reduced within 6months of the outbreak of the political issue. As such, it might beprudent to overstock the component now to avoid potential limitedsupplies in the future.

FIG. 68 is a diagram of an example of inventory management of anassembly line of building a product by monitoring various evaluationdata for component Ψ. The evaluation data includes evaluation data 1,evaluation data 2, evaluation data λ, pricing information, and supply todemand ratio. The grey areas in the past section are reflective of whenthe data is exhibiting a recognizable characteristic (e.g., pattern,trend, value, function result, etc.). Note that the time frame may beseconds, minutes, hours, days, weeks, months, years, etc.

The various evaluation data may be analyzed individually, collectively,or in any combination for one or more characteristics. For example,characteristic 1 may be a pattern of evaluation data λ that has anincreasing value for a period of time at a desired slope. As anotherexample, characteristic 2 may be a function executed upon evaluationdata 1 and 2. As yet another example, characteristic 3 may based on atrend of the price evaluation data. As a further example, thecharacteristic 4 may be based on a functional result of all of theevaluation data.

The identifiable characteristics of the past are used to identifysimilar characteristics in the future, which can be used to setindicators for a limit table. Such limit tables may then be used tomanage inventory based on recognizable characteristics and probablesubsequent characteristics.

FIG. 69 is a diagram of an example of graphical user interface (GUI) foran asset modification process. The GUI includes a plurality of windows.Some of the windows may be used to display a graphical representation ofevaluation data of interest. One or more other windows may be used toillustrate the processing of a limit table 724. Another window may beused to illustrate a graphical representation of the operation data(e.g., asset modification) performance (e.g., increasing, reducing,etc.) 726. Another window may be used for a graphical representation ofa control panel 728.

The control panel 728 may include buttons for stopping the execution ofa limit table 730, adding another asset to the processing of the currentlimit table 732, removing an asset from the processing of the currentlimit table 734, changing the limit table 736, selecting a new asset anda new limit table for modification 738. Other controls may be includedto build a limit table, selecting a limit table, selecting an asset,etc.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. An asset modification device comprises: memoryoperable to store: a plurality of limit tables; and a plurality of assetoperational data, wherein an asset operational data of the plurality ofasset operational data is regarding a particular asset of a plurality ofassets; an asset modification module; and a plurality of operationmodules, wherein each of the plurality of operation modules performs aunique task, wherein an operation module of the plurality of operationmodules includes: an evaluation data filter; and a specific taskexecution module; wherein the asset modification module is operable to:select an asset of the plurality of assets for modification; select alimit table of the plurality of limit tables regarding the modificationof the asset; select one of the plurality of operation modules based onan entry in the limit table to provide a selected operation module; andselect evaluation data for the one of the plurality of operationsmodules based on the entry in the limit table; and wherein the specifictask execution module of the selected operation module executes aspecific task on asset operational data of the asset to produce amodified asset when the evaluation data filter of the selected operationmodule indicates that analysis of the evaluation data is favorable formodification of the asset via the specific task.
 2. The assetmodification device of claim 1, wherein the asset comprises at least oneof: tangible property, financial capital, intangible assets,intelligence information, a rented asset, and a disposable asset.
 3. Theasset modification device of claim 1, wherein the selected operationmodule further comprises: a trigger/detrigger module operable to:trigger the specific task execution module when analysis of theevaluation data is favorable in view of one or more trigger indicators;detrigger the specific task execution module when analysis of theevaluation data is favorable in view of one or more detriggerindicators; and an activate/deactivate module operable to: when thespecific task execution module is triggered, activate the specific taskexecution module when the analysis of the evaluation data is favorablein view of one or more activate indicators; and deactivate the specifictask execution module when the analysis of the evaluation data isfavorable in view of one or more deactivate indicators.
 4. The assetmodification device of claim 1, wherein the asset modification module isfurther operable to select the limit table by at least one of: a desiredmodification; and a selected asset.
 5. The asset modification device ofclaim 1 further comprises: the asset modification module operable to:interpret limit table information of the selected limit table todetermine inputs for the selected operation module; and the selectedoperation module operable to: receive the inputs; utilize, by theevaluation data filter, at least one of the inputs to: identify theevaluation data; and filter the evaluation data to produce filteredevaluation data; and utilize, by the specific task execution module, atleast another one of the inputs to execute the specific task on theasset operational data.
 6. The asset modification device of claim 1,wherein the specific task comprises at least one of: a logic function, amathematical function, an algorithm, and an operational instruction. 7.The asset modification device of claim 1, wherein the asset modificationmodule is further operable to: receive a result from the selectedoperation module; interpret, via a results analysis module of the assetmodification module, the result to determine a next state of modifyingthe asset, wherein the next state includes one of: a deactivated state;a detriggered state; a triggered state; a transition state; a nextsequence state; and provide, via the results analysis module, updatedstatus information regarding the next state to the limit table.
 8. Theasset modification device of claim 1, wherein the selected operationmodule further comprises: a second specific task execution module; anddetermines whether the specific task execution module or the secondspecific task execution module executes the specific task.
 9. The assetmodification device of claim 1, wherein the asset modification module isfurther operable to: interpret limit table information to determinesecond inputs for another operation module; and the another operationmodule operable to: receive the second inputs; utilize, by an evaluationdata filter, of the other operation module, at least one of the secondinputs to: identify second evaluation data; and filter the secondevaluation data to produce filtered evaluation data; and utilize, by aspecific task execution module of the other operation module, at leastanother one of the second inputs to execute a second specific task onthe asset operational data.
 10. A device comprises: an interface; and anoperational processing module that includes: an evaluation data filter;filtered data analysis module; and a specific task execution module;wherein the operational processing module is operable to: receive, viathe interface, selected evaluation data; receive, via the interface,performance criteria; receive, via the interface, operational dataregarding an asset being modified; receive, via the interface,evaluation criteria; filter, by the evaluation data filter, the selectedevaluation data based on the evaluation criteria to produce filtereddata; determine, by the filtered data analysis module, whether toactivate the specific task execution module based on an analysis of thefiltered data in view of the performance criteria; and execute, by thespecific task execution module, a specific task on the operational datato produce a result for modifying the asset when the filtered dataanalysis module activates the specific task execution module.
 11. Thedevice of claim 10, wherein the asset comprises at least one of:tangible property, financial capital, intangible assets, intelligenceinformation, a rented asset, and a disposable asset.
 12. The device ofclaim 10, wherein the filtered data analysis module comprises: atrigger/detrigger module operable to: trigger the specific taskexecution module when analysis of the evaluation data is favorable inview of one or more trigger indicators; detrigger the specific taskexecution module when analysis of the evaluation data is favorable inview of one or more detrigger indicators; and an activate/deactivatemodule; and activate the specific task execution module when theanalysis of the evaluation data is favorable in view of one or moreactivate indicators; and deactivate the specific task execution modulewhen the analysis of the evaluation data is favorable in view of one ormore deactivate indicators.
 13. The device of claim 10, wherein thespecific task comprises one of: a logic function, a mathematicalfunction, an algorithm, and an operational instruction.
 14. The deviceof claim 10, wherein the operational processing module is furtheroperable to: send the result to an asset modification module, whichmodifies the asset based on the result.
 15. The device of claim 10,wherein the interface comprises at least one of: a WLAN interface; a WANinterface; a LAN interface; and a local computer hardware interface.